Tag Archives: Natural Language Processing

Progress and Challenges in Long-Form Open-Domain Question Answering

Open-domain long-form question answering (LFQA) is a fundamental challenge in natural language processing (NLP) that involves retrieving documents relevant to a given question and using them to generate an elaborate paragraph-length answer. While there has been remarkable recent progress in factoid open-domain question answering (QA), where a short phrase or entity is enough to answer a question, much less work has been done in the area of long-form question answering. LFQA is nevertheless an important task, especially because it provides a testbed to measure the factuality of generative text models. But, are current benchmarks and evaluation metrics really suitable for making progress on LFQA?

In “Hurdles to Progress in Long-form Question Answering” (to appear at NAACL 2021), we present a new system for open-domain long-form question answering that leverages two recent advances in NLP: 1) state-of-the-art sparse attention models, such as Routing Transformer (RT), which allow attention-based models to scale to long sequences, and 2) retrieval-based models, such as REALM, which facilitate retrievals of Wikipedia articles related to a given query. To encourage more factual grounding, our system combines information from several retrieved Wikipedia articles related to the given question before generating an answer. It achieves a new state of the art on ELI5, the only large-scale publicly available dataset for long-form question answering.

However, while our system tops the public leaderboard, we discover several troubling trends with the ELI5 dataset and its associated evaluation metrics. In particular, we find 1) little evidence that models actually use the retrievals on which they condition; 2) that trivial baselines (e.g., input copying) beat modern systems, like RAG / BART+DPR; and 3) that there is a significant train/validation overlap in the dataset. Our paper suggests mitigation strategies for each of these issues.

Text Generation
The main workhorse of NLP models is the Transformer architecture, in which each token in a sequence attends to every other token in a sequence, resulting in a model that scales quadratically with sequence length. The RT model introduces a dynamic, content-based sparse attention mechanism that reduces the complexity of attention in the Transformer model from n2 to n1.5, where n is the sequence length, which enables it to scale to long sequences. This allows each word to attend to other relevant words anywhere in the entire piece of text, unlike methods such as Transformer-XL where a word can only attend to words in its immediate vicinity.

The key insight of the RT work is that each token attending to every other token is often redundant, and may be approximated by a combination of local and global attention. Local attention allows each token to build up a local representation over several layers of the model, where each token attends to a local neighborhood, facilitating local consistency and fluency. Complementing local attention, the RT model also uses mini-batch k-means clustering to enable each token to attend only to a set of most relevant tokens.

Attention maps for the content-based sparse attention mechanism used in Routing Transformer. The word sequence is represented by the diagonal dark colored squares. In the Transformer model (left), each token attends to every other token. The shaded squares represent the tokens in the sequence to which a given token (the dark square) is attending. The RT model uses both local attention (middle), where tokens attend only to other tokens in their local neighborhood, and routing attention (right), in which a token only attends to clusters of tokens most relevant to it in context. The dark red, green and blue tokens only attend to the corresponding color of lightly shaded tokens.

We pre-train an RT model on the Project Gutenberg (PG-19) data-set with a language modeling objective, i.e, the model learns to predict the next word given all the previous words, so as to be able to generate fluent paragraph long text.

Information Retrieval
To demonstrate the effectiveness of the RT model on the task of LFQA, we combine it with retrievals from REALM. The REALM model (Guu et al. 2020) is a retrieval-based model that uses the maximum inner product search to retrieve Wikipedia articles relevant to a particular query or question. The model was fine-tuned for factoid-based question answering on the Natural Questions dataset. REALM utilizes the BERT model to learn good representations for a question and uses SCANN to retrieve Wikipedia articles that have a high topical similarity with the question representation. This is then trained end-to-end to maximize the log-likelihood on the QA task.

We further improve the quality of REALM retrievals by using a contrastive loss. The idea behind this is to encourage the representation of a question to get close to its ground truth answer and diverge from the other answers in its mini-batch. This ensures that when the system retrieves relevant items using this question representation, it returns articles that are "similar" to ground truth answers. We call this retriever contrastive-REALM or c-REALM.

The combined RT + c-REALM system for LFQA.

Evaluation
We test the model on long-form question answering using the ELI5 dataset, which is a part of the KILT benchmark, and is the only publicly available large-scale LFQA dataset. The KILT benchmark measures text retrievals using Precision (R-Prec) and text generation using ROUGE-L. The two scores are combined to give a KILT R-L score, which determines a model’s ranking on the leaderboard. We fine-tune the pre-trained RT model together with retrievals from c-REALM on the ELI5 dataset from KILT.

Our submission tops the KILT leaderboard for long-form question answering on ELI5 with a combined KILT R-L score of 2.36. It improves on the previous leaderboard entry of BART + DPR (KILT R-L score of 1.9), while having a similar number of parameters as the other models on the leaderboard. In terms of text generation quality, we see an improvement of +4.11, +5.78 and +9.14 Rouge-L over T5, BART + DPR and RAG, respectively.

Our results on the KILT leaderboard for long form question answering on ELI5.

Example Generations from the RT Model

  • Q: Why are almost all boats white?

    Boats are generally painted white for obvious reasons, as they are inexpensive and easy to clean. Ships are also often painted to provide a visible visual contrast to the sea. Boats also often have high visibility ports to allow for easy international trade. In addition, many boats have an air-breathing function that makes them easier to spot and stay afloat, which is a good thing if you're a pirate or trying to get into some country's waters. Lastly, many ships are built in the last few decades, so they are not as exotic looking as they were thirty years ago. And lastly, many ships are made out of materials that are easy to work with, and so many of them are painted white as a practical matter. There are many, many more reasons for why ships are white, but those are the ones I know of. I'm sure there are many other reasons, but those are the ones I know of. Edit: I forgot to mention that the other answers about visibility and easy to see ports are also true. Ships are painted to appear to be larger, and more expensive. That makes them appear more impressive, and so they are more likely to be bought by people who want to show off their wealth and status.
  • Q: What causes the trail behind jets at high altitude?

    The same thing that causes the clouds you see at the start of a storm to form in the first place. The air pressure changes as the jet moves through the air, causing it to cool down and condense into water droplets. These droplets are in fact a lot bigger than the water droplets in the clouds, so when the plane passes through them, they can cause the air pressure to change, causing the water droplets to condense into clouds. This is what you see when you look at a cloud formation from the ground. In addition to this, when the plane passes through the clouds, it also moves through the air, causing it to change its temperature, causing the water droplets to condense and form clouds. These clouds are often quite large, and can block out the sun. This is what you see when you look at a contrail. Finally, the water vapor that forms at high altitudes is cooled by the air above, and as it cools, it condenses into clouds. This is what you see when you look at a cumulonimbus cloud. These clouds are even larger than the water droplets in the clouds, and they block out the sun.
  • Q: How do shampoo and conditioner work?

    The key ingredients in shampoo and conditioner are usually surfactants, they tend to attract oil from your skin. Shampoo will usually contain an anti-dandruff agent (dandruff is caused by fungi in your skin which cause dandruff). They also contain some moisturizing agents (moisturizers are things that stay in your skin longer and protect your scalp from cleaning), and a lot of anti-dandruff (dandruff is a side effect of high doses of both surfactants and moisturizers) Conditioner will contain moisturizing agents, like a skin moisturizer. They tend to cause oils to be separated from your hair (e.g. wash your hair with conditioner instead of shampoo). They both have a foaming agent that makes them foam up (usually Hydrogen Peroxide). They also have a foaming agent that stops dirt from attaching to your hair. Some conditioner will also suppress the action of the shampoo (to help prevent dirt from attaching to your hair).
  • Q: When glancing at a clock, why does the first second after glancing at it sometimes feel longer than the rest?

    This is known as chronostasis. Your brain gets used to seeing the second hand move after the first, so it ignores the first one. If you were to look at the clock a lot, you'd see the second hand move a lot more than the first, and you'd be able to tell the difference between the second and the first. The second hand will appear to move much slower than the first hand because your brain is used to seeing the second hand move a lot more than the first. If you were to go on a long trip, you'd get used to the second hand moving a lot more than the first, and you'd have no problem telling the difference between the second and the first hand. It's kind of like how you don't notice your nose when you're driving, but if you look at it for a while, you start to notice it. Your brain is just getting used to seeing the nose move a lot more than the other, and it can't tell the difference between the two. It's kind of like that with your eyes.
  • Q: Why do Britain and other English empire countries still bow to monarchs? What real purpose does the queen serve?

    Depends what you define as "purpose". There is no specific purpose for the British monarch - rather, they are figureheads that serve a role. The role is to have a high degree of influence over a country, though the rules of the monarchy can somewhat vary from country to country, and the ceremony of "kissing the royal corpse" does not always involve kissing the monarch. Whether that's the only reason for the custom, or if it was the only reason, I can't say, but that's the reasoning. When the U.K. was in full power, the monarch was a puppet of Parliament, and the powers of the monarch were transferred to the Prime Minister and thus to the Prime Minister's deputy, who then became the Prime Minister. Since then, Parliament has been able to vote on legislation that goes through the monarch, although they may still act as the monarch's representative in negotiating treaties, which can have very very deep consequences. The Queen's role, as a representative of Britain, doesn't necessarily involve her formal approval of any of the laws or legislation that goes through Parliament, though.
  • Q: What exactly is fire, in detail? How can light and heat come from something we can't really touch?

    Fire is the chemical reaction of fuel (oxygen) with (what we call) impurities (ash, soot, oil etc). These impurities are created due to a number of things. If you were to study the chemical composition of a log (or a candle) and analyse it you would discover the chemical formula (A, C, and G) which is a simplified version of what actually happens. There is always fuel, and there is always the ash/soot. Wood is made up of mostly carbon and hydrogen and these are the fuels that create the fire. Hydrogen and oxygen are reactive, and they form water (H2O) and CO2 (CO2). If we split a log into smaller pieces, we start to see the process of combustion as it occurs. The carbon is burned, and with the explosion of CO, H2O and H2CO2, we get water and carbon dioxide (mostly). If we smash a log together, we start to see what happens when we also hit another log with another log...imagine smashing two sand castles together. We see how the sand castles and brick castles form and collapse at different rates...then it starts to burn. The smoke, ash and flames are the actual combustion of fuel.
  • Q: What goes on in those tall tower buildings owned by major banks?

    The actual buildings are not there for profit but for show. There are many real world historical buildings that have little to no effect other than being a symbol of an economic entity or symbol of an era or location, and nothing more. For example look at Sears, Sears didn't care what went on inside, it was all about the \_appearance\_ of its location, the prestige of the location, the facilities and so on. It didn't care about how long it took it to operate, it was about how much people would pay to go see it. Sears was a landmark as a cultural movement and other big companies followed suit, so if you want to see a building you've never seen before, you have to go see Sears, just like you have to see a Toyota Camry for Toyota Camry. They used to be all about building new factories, some of them if I recall, but now that they're bigger, that means that more factory jobs are coming to them. You've probably seen them in stores as stores where people buy and sell stuff, so there aren't that many places for them to come from. Instead, it's just for show, a symbol of rich people.

Hurdles Towards Progress in LFQA
However, while the RT system described here tops the public leaderboard, a detailed analysis of the model and the ELI5 dataset reveal some concerning trends.

  • Many held-out questions are paraphrased in the training set. Best answer to similar train questions gets 27.4 ROUGE-L.

  • Simply retrieving answers to random unrelated training questions yields relatively high ROUGE-L, while actual gold answers underperform generations.

  • Conditioning answer generation on random documents instead of relevant ones does not measurably impact its factual correctness. Longer outputs get higher ROUGE-L.

We find little to no evidence that the model is actually grounding its text generation in the retrieved documents — fine-tuning an RT model with random retrievals from Wikipedia (i.e., random retrieval + RT) performs nearly as well as the c-REALM + RT model (24.2 vs 24.4 ROUGE-L). We also find significant overlap in the training, validation and test sets of ELI5 (with several questions being paraphrases of each other), which may eliminate the need for retrievals. The KILT benchmark measures the quality of retrievals and generations separately, without making sure that the text generation actually use the retrievals.

Trivial baselines get higher Rouge-L scores than RAG and BART + DPR.

Moreover, we find issues with the Rouge-L metric used to evaluate the quality of text generation, with trivial nonsensical baselines, such as a Random Training Set answer and Input Copying, achieving relatively high Rouge-L scores (even beating BART + DPR and RAG).

Conclusion
We proposed a system for long form-question answering based on Routing Transformers and REALM, which tops the KILT leaderboard on ELI5. However, a detailed analysis reveals several issues with the benchmark that preclude using it to inform meaningful modelling advances. We hope that the community works together to solve these issues so that researchers can climb the right hills and make meaningful progress in this challenging but important task.

Acknowledgments
The Routing Transformer work has been a team effort involving Aurko Roy, Mohammad Saffar, Ashish Vaswani and David Grangier. The follow-up work on open-domain long-form question answering has been a collaboration involving Kalpesh Krishna, Aurko Roy and Mohit Iyyer. We wish to thank Vidhisha Balachandran, Niki Parmar and Ashish Vaswani for several helpful discussions, and the REALM team (Kenton Lee, Kelvin Guu, Ming-Wei Chang and Zora Tung) for help with their codebase and several useful discussions, which helped us improve our experiments. We are grateful to Tu Vu for help with the QQP classifier used to detect paraphrases in ELI5 train and test sets. We thank Jules Gagnon-Marchand and Sewon Min for suggesting useful experiments on checking ROUGE-L bounds. Finally we thank Shufan Wang, Andrew Drozdov, Nader Akoury and the rest of the UMass NLP group for helpful discussions and suggestions at various stages in the project.

Source: Google AI Blog


Learning to Reason Over Tables from Less Data

The task of recognizing textual entailment, also known as natural language inference, consists of determining whether a piece of text (a premise), can be implied or contradicted (or neither) by another piece of text (the hypothesis). While this problem is often considered an important test for the reasoning skills of machine learning (ML) systems and has been studied in depth for plain text inputs, much less effort has been put into applying such models to structured data, such as websites, tables, databases, etc. Yet, recognizing textual entailment is especially relevant whenever the contents of a table need to be accurately summarized and presented to a user, and is essential for high fidelity question answering systems and virtual assistants.

In "Understanding tables with intermediate pre-training", published in Findings of EMNLP 2020, we introduce the first pre-training tasks customized for table parsing, enabling models to learn better, faster and from less data. We build upon our earlier TAPAS model, which was an extension of the BERT bi-directional Transformer model with special embeddings to find answers in tables. Applying our new pre-training objectives to TAPAS yields a new state of the art on multiple datasets involving tables. On TabFact, for example, it reduces the gap between model and human performance by ~50%. We also systematically benchmark methods of selecting relevant input for higher efficiency, achieving 4x gains in speed and memory, while retaining 92% of the results. All the models for different tasks and sizes are released on GitHub repo, where you can try them out yourself in a colab Notebook.

Textual Entailment
The task of textual entailment is more challenging when applied to tabular data than plain text. Consider, for example, a table from Wikipedia with some sentences derived from its associated table content. Assessing if the content of the table entails or contradicts the sentence may require looking over multiple columns and rows, and possibly performing simple numeric computations, like averaging, summing, differencing, etc.

A table together with some statements from TabFact. The content of the table can be used to support or contradict the statements.

Following the methods used by TAPAS, we encode the content of a statement and a table together, pass them through a Transformer model, and obtain a single number with the probability that the statement is entailed or refuted by the table.

The TAPAS model architecture uses a BERT model to encode the statement and the flattened table, read row by row. Special embeddings are used to encode the table structure. The vector output of the first token is used to predict the probability of entailment.

Because the only information in the training examples is a binary value (i.e., "correct" or "incorrect"), training a model to understand whether a statement is entailed or not is challenging and highlights the difficulty in achieving generalization in deep learning, especially when the provided training signal is scarce. Seeing isolated entailed or refuted examples, a model can easily pick-up on spurious patterns in the data to make a prediction, for example the presence of the word "tie" in "Greg Norman and Billy Mayfair tie in rank", instead of truly comparing their ranks, which is what is needed to successfully apply the model beyond the original training data.

Pre-training Tasks
Pre-training tasks can be used to “warm-up” models by providing them with large amounts of readily available unlabeled data. However, pre-training typically includes primarily plain text and not tabular data. In fact, TAPAS was originally pre-trained using a simple masked language modelling objective that was not designed for tabular data applications. In order to improve the model performance on tabular data, we introduce two novel pretraining binary-classification tasks called counterfactual and synthetic, which can be applied as a second stage of pre-training (often called intermediate pre-training).

In the counterfactual task, we source sentences from Wikipedia that mention an entity (person, place or thing) that also appears in a given table. Then, 50% of the time, we modify the statement by swapping the entity for another alternative. To make sure the statement is realistic, we choose a replacement among the entities in the same column in the table. The model is trained to recognize whether the statement was modified or not. This pre-training task includes millions of such examples, and although the reasoning about them is not complex, they typically will still sound natural.

For the synthetic task, we follow a method similar to semantic parsing in which we generate statements using a simple set of grammar rules that require the model to understand basic mathematical operations, such as sums and averages (e.g., "the sum of earnings"), or to understand how to filter the elements in the table using some condition (e.g.,"the country is Australia"). Although these statements are artificial, they help improve the numerical and logical reasoning skills of the model.

Example instances for the two novel pre-training tasks. Counterfactual examples swap entities mentioned in a sentence that accompanies the input table for a plausible alternative. Synthetic statements use grammar rules to create new sentences that require combining the information of the table in complex ways.

Results
We evaluate the success of the counterfactual and synthetic pre-training objectives on the TabFact dataset by comparing to the baseline TAPAS model and to two prior models that have exhibited success in the textual entailment domain, LogicalFactChecker (LFC) and Structure Aware Transformer (SAT). The baseline TAPAS model exhibits improved performance relative to LFC and SAT, but the pre-trained model (TAPAS+CS) performs significantly better, achieving a new state of the art.

We also apply TAPAS+CS to question answering tasks on the SQA dataset, which requires that the model find answers from the content of tables in a dialog setting. The inclusion of CS objectives improves the previous best performance by more than 4 points, demonstrating that this approach also generalizes performance beyond just textual entailment.

Results on TabFact (left) and SQA (right). Using the synthetic and counterfactual datasets, we achieve new state-of-the-art results in both tasks by a large margin.

Data and Compute Efficiency
Another aspect of the counterfactual and synthetic pre-training tasks is that since the models are already tuned for binary classification, they can be applied without any fine-tuning to TabFact. We explore what happens to each of the models when trained only on a subset (or even none) of the data. Without looking at a single example, the TAPAS+CS model is competitive with a strong baseline Table-Bert, and when only 10% of the data are included, the results are comparable to the previous state-of-the-art.

Dev accuracy on TabFact relative to the fraction of the training data used.

A general concern when trying to use large models such as this to operate on tables, is that their high computational requirements makes it difficult for them to parse very large tables. To address this, we investigate whether one can heuristically select subsets of the input to pass through the model in order to optimize its computational efficiency.

We conducted a systematic study of different approaches to filter the input and discovered that simple methods that select for word overlap between a full column and the subject statement give the best results. By dynamically selecting which tokens of the input to include, we can use fewer resources or work on larger inputs at the same cost. The challenge is doing so without losing important information and hurting accuracy. 

For instance, the models discussed above all use sequences of 512 tokens, which is around the normal limit for a transformer model (although recent efficiency methods like the Reformer or Performer are proving effective in scaling the input size). The column selection methods we propose here can allow for faster training while still achieving high accuracy on TabFact. For 256 input tokens we get a very small drop in accuracy, but the model can now be pre-trained, fine-tuned and make predictions up to two times faster. With 128 tokens the model still outperforms the previous state-of-the-art model, with an even more significant speed-up — 4x faster across the board.

Accuracy on TabFact using different sequence lengths, by shortening the input with our column selection method.

Using both the column selection method we proposed and the novel pre-training tasks, we can create table parsing models that need fewer data and less compute power to obtain better results.

We have made available the new models and pre-training techniques at our GitHub repo, where you can try it out yourself in colab. In order to make this approach more accessible, we also shared models of varying sizes all the way down to “tiny”. It is our hope that these results will help spur development of table reasoning among the broader research community.

Acknowledgements
This work was carried out by Julian Martin Eisenschlos, Syrine Krichene and Thomas Müller from our Language Team in Zürich. We would like to thank Jordan Boyd-Graber, Yasemin Altun, Emily Pitler, Benjamin Boerschinger, Srini Narayanan, Slav Petrov, William Cohen and Jonathan Herzig for their useful comments and suggestions.

Source: Google AI Blog


Improving Indian Language Transliterations in Google Maps

Nearly 75% of India’s population — which possesses the second highest number of internet users in the world — interacts with the web primarily using Indian languages, rather than English. Over the next five years, that number is expected to rise to 90%. In order to make Google Maps as accessible as possible to the next billion users, it must allow people to use it in their preferred language, enabling them to explore anywhere in the world.

However, the names of most Indian places of interest (POIs) in Google Maps are not generally available in the native scripts of the languages of India. These names are often in English and may be combined with acronyms based on the Latin script, as well as Indian language words and names. Addressing such mixed-language representations requires a transliteration system that maps characters from one script to another, based on the source and target languages, while accounting for the phonetic properties of the words as well.

For example, consider a user in Ahmedabad, Gujarat, who is looking for a nearby hospital, KD Hospital. They issue the search query, કેડી હોસ્પિટલ, in the native script of Gujarati, the 6th most widely spoken language in India. Here, કેડી (“kay-dee”) is the sounding out of the acronym KD, and હોસ્પિટલ is “hospital”. In this search, Google Maps knows to look for hospitals, but it doesn't understand that કેડી is KD, hence it finds another hospital, CIMS. As a consequence of the relative sparsity of names available in the Gujarati script for places of interest (POIs) in India, instead of their desired result, the user is shown a result that is further away.


To address this challenge, we have built an ensemble of learned models to transliterate names of Latin script POIs into 10 languages prominent in India: Hindi, Bangla, Marathi, Telugu, Tamil, Gujarati, Kannada, Malayalam, Punjabi, and Odia. Using this ensemble, we have added names in these languages to millions of POIs in India, increasing the coverage nearly twenty-fold in some languages. This will immediately benefit millions of existing Indian users who don't speak English, enabling them to find doctors, hospitals, grocery stores, banks, bus stops, train stations and other essential services in their own language.

Transliteration vs. Transcription vs. Translation
Our goal was to design a system that will transliterate from a reference Latin script name into the scripts and orthographies native to the above-mentioned languages. For example, the Devanagari script is the native script for both Hindi and Marathi (the language native to Nagpur, Maharashtra). Transliterating the Latin script names for NIT Garden and Chandramani Garden, both POIs in Nagpur, results in एनआईटी गार्डन and चंद्रमणी गार्डन, respectively, depending on the specific language’s orthography in that script.

It is important to note that the transliterated POI names are not translations. Transliteration is only concerned with writing the same words in a different script, much like an English language newspaper might choose to write the name Горбачёв from the Cyrillic script as “Gorbachev” for their readers who do not read the Cyrillic script. For example, the second word in both of the transliterated POI names above is still pronounced “garden”, and the second word of the Gujarati example earlier is still “hospital” — they remain the English words “garden” and “hospital”, just written in the other script. Indeed, common English words are frequently used in POI names in India, even when written in the native script. How the name is written in these scripts is largely driven by its pronunciation; so एनआईटी from the acronym NIT is pronounced “en-aye-tee”, not as the English word “nit”. Knowing that NIT is a common acronym from the region is one piece of evidence that can be used when deriving the correct transliteration.

Note also that, while we use the term transliteration, following convention in the NLP community for mapping directly between writing systems, romanization in South Asian languages regardless of the script is generally pronunciation-driven, and hence one could call these methods transcription rather than transliteration. The task remains, however, mapping between scripts, since pronunciation is only relatively coarsely captured in the Latin script for these languages, and there remain many script-specific correspondences that must be accounted for. This, coupled with the lack of standard spelling in the Latin script and the resulting variability, is what makes the task challenging.

Transliteration Ensemble
We use an ensemble of models to automatically transliterate from the reference Latin script name (such as NIT Garden or Chandramani Garden) into the scripts and orthographies native to the above-mentioned languages. Candidate transliterations are derived from a pair of sequence-to-sequence (seq2seq) models. One is a finite-state model for general text transliteration, trained in a manner similar to models used by Gboard on-device for transliteration keyboards. The other is a neural long short-term memory (LSTM) model trained, in part, on the publicly released Dakshina dataset. This dataset contains Latin and native script data drawn from Wikipedia in 12 South Asian languages, including all but one of the languages mentioned above, and permits training and evaluation of various transliteration methods. Because the two models have such different characteristics, together they produce a greater variety of transliteration candidates.

To deal with the tricky phenomena of acronyms (such as the “NIT” and “KD” examples above), we developed a specialized transliteration module that generates additional candidate transliterations for these cases.

For each native language script, the ensemble makes use of specialized romanization dictionaries of varying provenance that are tailored for place names, proper names, or common words. Examples of such romanization dictionaries are found in the Dakshina dataset.

Scoring in the Ensemble
The ensemble combines scores for the possible transliterations in a weighted mixture, the parameters of which are tuned specifically for POI name accuracy using small targeted development sets for such names.

For each native script token in candidate transliterations, the ensemble also weights the result according to its frequency in a very large sample of on-line text. Additional candidate scoring is based on a deterministic romanization approach derived from the ISO 15919 romanization standard, which maps each native script token to a unique Latin script string. This string allows the ensemble to track certain key correspondences when compared to the original Latin script token being transliterated, even though the ISO-derived mapping itself does not always perfectly correspond to how the given native script word is typically written in the Latin script.

In aggregate, these many moving parts provide substantially higher quality transliterations than possible for any of the individual methods alone.

Coverage
The following table provides the per-language quality and coverage improvements due to the ensemble over existing automatic transliterations of POI names. The coverage improvement measures the increase in items for which an automatic transliteration has been made available. Quality improvement measures the ratio of updated transliterations that were judged to be improvements versus those that were judged to be inferior to existing automatic transliterations.

  Coverage Quality
Language   Improvement    Improvement
Hindi 3.2x 1.8x
Bengali 19x 3.3x
Marathi 19x 2.9x
Telugu 3.9x 2.6x
Tamil 19x 3.6x
Gujarati 19x 2.5x
Kannada 24x 2.3x
Malayalam 24x 1.7x
Odia 960x *
Punjabi 24x *
* Unknown / No Baseline.

Conclusion
As with any machine learned system, the resulting automatic transliterations may contain a few errors or infelicities, but the large increase in coverage in these widely spoken languages marks a substantial expansion of the accessibility of information within Google Maps in India. Future work will include using the ensemble for transliteration of other classes of entities within Maps and its extension to other languages and scripts, including Perso-Arabic scripts, which are also commonly used in the region.

Acknowledgments
This work was a collaboration between the authors and Jacob Farner, Jonathan Herbert, Anna Katanova, Andre Lebedev, Chris Miles, Brian Roark, Anurag Sharma, Kevin Wang, Andy Wildenberg, and many others.

Source: Google AI Blog


The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models

As natural language processing (NLP) models become more powerful and are deployed in more real-world contexts, understanding their behavior is becoming increasingly critical. While advances in modeling have brought unprecedented performance on many NLP tasks, many research questions remain about not only the behavior of these models under domain shift and adversarial settings, but also their tendencies to behave according to social biases or shallow heuristics.

For any new model, one might want to know in which cases a model performs poorly, why a model makes a particular prediction, or whether a model will behave consistently under varying inputs, such as changes to textual style or pronoun gender. But, despite the recent explosion of work on model understanding and evaluation, there is no “silver bullet” for analysis. Practitioners must often experiment with many techniques, looking at local explanations, aggregate metrics, and counterfactual variations of the input to build a better understanding of model behavior, with each of these techniques often requiring its own software package or bespoke tool. Our previously released What-If Tool was built to address this challenge by enabling black-box probing of classification and regression models, thus enabling researchers to more easily debug performance and analyze the fairness of machine learning models through interaction and visualization. But there was still a need for a toolkit that would address challenges specific to NLP models.

With these challenges in mind, we built and open-sourced the Language Interpretability Tool (LIT), an interactive platform for NLP model understanding. LIT builds upon the lessons learned from the What-If Tool with greatly expanded capabilities, which cover a wide range of NLP tasks including sequence generation, span labeling, classification and regression, along with customizable and extensible visualizations and model analysis.

LIT supports local explanations, including salience maps, attention, and rich visualizations of model predictions, as well as aggregate analysis including metrics, embedding spaces, and flexible slicing. It allows users to easily hop between visualizations to test local hypotheses and validate them over a dataset. LIT provides support for counterfactual generation, in which new data points can be added on the fly, and their effect on the model visualized immediately. Side-by-side comparison allows for two models, or two individual data points, to be visualized simultaneously. More details about LIT can be found in our system demonstration paper, which was presented at EMNLP 2020.

Exploring a sentiment classifier with LIT.

Customizability
In order to better address the broad range of users with different interests and priorities that we hope will use LIT, we’ve built the tool to be easily customizable and extensible from the start. Using LIT on a particular NLP model and dataset only requires writing a small bit of Python code. Custom components, such as task-specific metrics calculations or counterfactual generators, can be written in Python and added to a LIT instance through our provided APIs. Also, the front end itself can be customized, with new modules that integrate directly into the UI. For more on extending the tool, check out our documentation on GitHub.

Demos
To illustrate some of the capabilities of LIT, we have created a few demos using pre-trained models. The full list is available on the LIT website, and we describe two of them here:

  • Sentiment analysis: In this example, a user can explore a BERT-based binary classifier that predicts if a sentence has positive or negative sentiment. The demo uses the Stanford Sentiment Treebank of sentences from movie reviews to demonstrate model behavior. One can examine local explanations using saliency maps provided by a variety of techniques (such as LIME and integrated gradients), and can test model behavior with perturbed (counterfactual) examples using techniques such as back-translation, word replacement, or adversarial attacks. These techniques can help pinpoint under what scenarios a model fails, and whether those failures are generalizable, which can then be used to inform how best to improve a model.
    Analyzing token-based salience of an incorrect prediction. The word “laughable” seems to be incorrectly raising the positive sentiment score of this example.
  • Masked word prediction: Masked language modeling is a "fill-in-the-blank" task, where the model predicts different words that could complete a sentence. For example, given the prompt, "I took my ___ for a walk", the model might predict a high score for "dog." In LIT one can explore this interactively by typing in sentences or choosing from a pre-loaded corpus, and then clicking specific tokens to see what a model like BERT understands about language, or about the world.
    Interactively selecting a token to mask, and viewing a language model's predictions.

LIT in Practice and Future Work
Although LIT is a new tool, we have already seen the value that it can provide for model understanding. Its visualizations can be used to find patterns in model behavior, such as outlying clusters in embedding space, or words with outsized importance to the predictions. Exploration in LIT can test for potential biases in models, as demonstrated in our case study of LIT exploring gender bias in a coreference model. This type of analysis can inform next steps in improving model performance, such as applying MinDiff to mitigate systemic bias. It can also be used as an easy and fast way to create an interactive demo for any NLP model.

Check out the tool either through our provided demos, or by bringing up a LIT server for your own models and datasets. The work on LIT has just started, and there are a number of new capabilities and refinements planned, including the addition of new interpretability techniques from cutting edge ML and NLP research. If there are other techniques that you’d like to see added to the tool, please let us know! Join our mailing list to stay up to date as LIT evolves. And as the code is open-source, we welcome feedback on and contributions to the tool.

Acknowledgments
LIT is a collaborative effort between the Google Research PAIR and Language teams. This post represents the work of the many contributors across Google, including Andy Coenen, Ann Yuan, Carey Radebaugh, Ellen Jiang, Emily Reif, Jasmijn Bastings, Kristen Olson, Leslie Lai, Mahima Pushkarna, Sebastian Gehrmann, and Tolga Bolukbasi. We would like to thank all those who contributed to the project, both inside and outside Google, and the teams that have piloted its use and provided valuable feedback.

Source: Google AI Blog


Measuring Gendered Correlations in Pre-trained NLP Models

Natural language processing (NLP) has seen significant progress over the past several years, with pre-trained models like BERT, ALBERT, ELECTRA, and XLNet achieving remarkable accuracy across a variety of tasks. In pre-training, representations are learned from a large text corpus, e.g., Wikipedia, by repeatedly masking out words and trying to predict them (this is called masked language modeling). The resulting representations encode rich information about language and correlations between concepts, such as surgeons and scalpels. There is then a second training stage, fine-tuning, in which the model uses task-specific training data to learn how to use the general pre-trained representations to do a concrete task, like classification. Given the broad adoption of these representations in many NLP tasks, it is crucial to understand the information encoded in them and how any learned correlations affect performance downstream, to ensure the application of these models aligns with our AI Principles.

In “Measuring and Reducing Gendered Correlations in Pre-trained Models” we perform a case study on BERT and its low-memory counterpart ALBERT, looking at correlations related to gender, and formulate a series of best practices for using pre-trained language models. We present experimental results over public model checkpoints and an academic task dataset to illustrate how the best practices apply, providing a foundation for exploring settings beyond the scope of this case study. We will soon release a series of checkpoints, Zari1, which reduce gendered correlations while maintaining state-of-the-art accuracy on standard NLP task metrics.

Measuring Correlations
To understand how correlations in pre-trained representations can affect downstream task performance, we apply a diverse set of evaluation metrics for studying the representation of gender. Here, we’ll discuss results from one of these tests, based on coreference resolution, which is the capability that allows models to understand the correct antecedent to a given pronoun in a sentence. For example, in the sentence that follows, the model should recognize his refers to the nurse, and not to the patient.

The standard academic formulation of the task is the OntoNotes test (Hovy et al., 2006), and we measure how accurate a model is at coreference resolution in a general setting using an F1 score over this data (as in Tenney et al. 2019). Since OntoNotes represents only one data distribution, we also consider the WinoGender benchmark that provides additional, balanced data designed to identify when model associations between gender and profession incorrectly influence coreference resolution. High values of the WinoGender metric (close to one) indicate a model is basing decisions on normative associations between gender and profession (e.g., associating nurse with the female gender and not male). When model decisions have no consistent association between gender and profession, the score is zero, which suggests that decisions are based on some other information, such as sentence structure or semantics.

BERT and ALBERT metrics on OntoNotes (accuracy) and WinoGender (gendered correlations). Low values on the WinoGender metric indicate that a model does not preferentially use gendered correlations in reasoning.

In this study, we see that neither the (Large) BERT or ALBERT public model achieves zero score on the WinoGender examples, despite achieving impressive accuracy on OntoNotes (close to 100%). At least some of this is due to models preferentially using gendered correlations in reasoning. This isn’t completely surprising: there are a range of cues available to understand text and it is possible for a general model to pick up on any or all of these. However, there is reason for caution, as it is undesirable for a model to make predictions primarily based on gendered correlations learned as priors rather than the evidence available in the input.

Best Practices
Given that it is possible for unintended correlations in pre-trained model representations to affect downstream task reasoning, we now ask: what can one do to mitigate any risk this poses when developing new NLP models?

  • It is important to measure for unintended correlations: Model quality may be assessed using accuracy metrics, but these only measure one dimension of performance, especially if the test data is drawn from the same distribution as the training data. For example, the BERT and ALBERT checkpoints have accuracy within 1% of each other, but differ by 26% (relative) in the degree to which they use gendered correlations for coreference resolution. This difference might be important for some tasks; selecting a model with low WinoGender score could be desirable in an application featuring texts about people in professions that may not conform to historical social norms, e.g., male nurses.
  • Be careful even when making seemingly innocuous configuration changes: Neural network model training is controlled by many hyperparameters that are usually selected to maximize some training objective. While configuration choices often seem innocuous, we find they can cause significant changes for gendered correlations, both for better and for worse. For example, dropout regularization is used to reduce overfitting by large models. When we increase the dropout rate used for pre-training BERT and ALBERT, we see a significant reduction in gendered correlations even after fine-tuning. This is promising since a simple configuration change allows us to train models with reduced risk of harm, but it also shows that we should be mindful and evaluate carefully when making any change in model configuration.
    Impact of increasing dropout regularization in BERT and ALBERT.
  • There are opportunities for general mitigations: A further corollary from the perhaps unexpected impact of dropout on gendered correlations is that it opens the possibility to use general-purpose methods for reducing unintended correlations: by increasing dropout in our study, we improve how the models reason about WinoGender examples without manually specifying anything about the task or changing the fine-tuning stage at all. Unfortunately, OntoNotes accuracy does start to decline as the dropout rate increases (which we can see in the BERT results), but we are excited about the potential to mitigate this in pre-training, where changes can lead to model improvements without the need for task-specific updates. We explore counterfactual data augmentation as another mitigation strategy with different tradeoffs in our paper.

What’s Next
We believe these best practices provide a starting point for developing robust NLP systems that perform well across the broadest possible range of linguistic settings and applications. Of course these techniques on their own are not sufficient to capture and remove all potential issues. Any model deployed in a real-world setting should undergo rigorous testing that considers the many ways it will be used, and implement safeguards to ensure alignment with ethical norms, such as Google's AI Principles. We look forward to developments in evaluation frameworks and data that are more expansive and inclusive to cover the many uses of language models and the breadth of people they aim to serve.

Acknowledgements
This is joint work with Xuezhi Wang, Ian Tenney, Ellie Pavlick, Alex Beutel, Jilin Chen, Emily Pitler, and Slav Petrov. We benefited greatly throughout the project from discussions with Fernando Pereira, Ed Chi, Dipanjan Das, Vera Axelrod, Jacob Eisenstein, Tulsee Doshi, and James Wexler.



1 Zari is an Afghan Muppet designed to show that ‘a little girl could do as much as everybody else’.

Source: Google AI Blog


Measuring Gendered Correlations in Pre-trained NLP Models

Natural language processing (NLP) has seen significant progress over the past several years, with pre-trained models like BERT, ALBERT, ELECTRA, and XLNet achieving remarkable accuracy across a variety of tasks. In pre-training, representations are learned from a large text corpus, e.g., Wikipedia, by repeatedly masking out words and trying to predict them (this is called masked language modeling). The resulting representations encode rich information about language and correlations between concepts, such as surgeons and scalpels. There is then a second training stage, fine-tuning, in which the model uses task-specific training data to learn how to use the general pre-trained representations to do a concrete task, like classification. Given the broad adoption of these representations in many NLP tasks, it is crucial to understand the information encoded in them and how any learned correlations affect performance downstream, to ensure the application of these models aligns with our AI Principles.

In “Measuring and Reducing Gendered Correlations in Pre-trained Models” we perform a case study on BERT and its low-memory counterpart ALBERT, looking at correlations related to gender, and formulate a series of best practices for using pre-trained language models. We present experimental results over public model checkpoints and an academic task dataset to illustrate how the best practices apply, providing a foundation for exploring settings beyond the scope of this case study. We will soon release a series of checkpoints, Zari1, which reduce gendered correlations while maintaining state-of-the-art accuracy on standard NLP task metrics.

Measuring Correlations
To understand how correlations in pre-trained representations can affect downstream task performance, we apply a diverse set of evaluation metrics for studying the representation of gender. Here, we’ll discuss results from one of these tests, based on coreference resolution, which is the capability that allows models to understand the correct antecedent to a given pronoun in a sentence. For example, in the sentence that follows, the model should recognize his refers to the nurse, and not to the patient.

The standard academic formulation of the task is the OntoNotes test (Hovy et al., 2006), and we measure how accurate a model is at coreference resolution in a general setting using an F1 score over this data (as in Tenney et al. 2019). Since OntoNotes represents only one data distribution, we also consider the WinoGender benchmark that provides additional, balanced data designed to identify when model associations between gender and profession incorrectly influence coreference resolution. High values of the WinoGender metric (close to one) indicate a model is basing decisions on normative associations between gender and profession (e.g., associating nurse with the female gender and not male). When model decisions have no consistent association between gender and profession, the score is zero, which suggests that decisions are based on some other information, such as sentence structure or semantics.

BERT and ALBERT metrics on OntoNotes (accuracy) and WinoGender (gendered correlations). Low values on the WinoGender metric indicate that a model does not preferentially use gendered correlations in reasoning.

In this study, we see that neither the (Large) BERT or ALBERT public model achieves zero score on the WinoGender examples, despite achieving impressive accuracy on OntoNotes (close to 100%). At least some of this is due to models preferentially using gendered correlations in reasoning. This isn’t completely surprising: there are a range of cues available to understand text and it is possible for a general model to pick up on any or all of these. However, there is reason for caution, as it is undesirable for a model to make predictions primarily based on gendered correlations learned as priors rather than the evidence available in the input.

Best Practices
Given that it is possible for unintended correlations in pre-trained model representations to affect downstream task reasoning, we now ask: what can one do to mitigate any risk this poses when developing new NLP models?

  • It is important to measure for unintended correlations: Model quality may be assessed using accuracy metrics, but these only measure one dimension of performance, especially if the test data is drawn from the same distribution as the training data. For example, the BERT and ALBERT checkpoints have accuracy within 1% of each other, but differ by 26% (relative) in the degree to which they use gendered correlations for coreference resolution. This difference might be important for some tasks; selecting a model with low WinoGender score could be desirable in an application featuring texts about people in professions that may not conform to historical social norms, e.g., male nurses.
  • Be careful even when making seemingly innocuous configuration changes: Neural network model training is controlled by many hyperparameters that are usually selected to maximize some training objective. While configuration choices often seem innocuous, we find they can cause significant changes for gendered correlations, both for better and for worse. For example, dropout regularization is used to reduce overfitting by large models. When we increase the dropout rate used for pre-training BERT and ALBERT, we see a significant reduction in gendered correlations even after fine-tuning. This is promising since a simple configuration change allows us to train models with reduced risk of harm, but it also shows that we should be mindful and evaluate carefully when making any change in model configuration.
    Impact of increasing dropout regularization in BERT and ALBERT.
  • There are opportunities for general mitigations: A further corollary from the perhaps unexpected impact of dropout on gendered correlations is that it opens the possibility to use general-purpose methods for reducing unintended correlations: by increasing dropout in our study, we improve how the models reason about WinoGender examples without manually specifying anything about the task or changing the fine-tuning stage at all. Unfortunately, OntoNotes accuracy does start to decline as the dropout rate increases (which we can see in the BERT results), but we are excited about the potential to mitigate this in pre-training, where changes can lead to model improvements without the need for task-specific updates. We explore counterfactual data augmentation as another mitigation strategy with different tradeoffs in our paper.

What’s Next
We believe these best practices provide a starting point for developing robust NLP systems that perform well across the broadest possible range of linguistic settings and applications. Of course these techniques on their own are not sufficient to capture and remove all potential issues. Any model deployed in a real-world setting should undergo rigorous testing that considers the many ways it will be used, and implement safeguards to ensure alignment with ethical norms, such as Google's AI Principles. We look forward to developments in evaluation frameworks and data that are more expansive and inclusive to cover the many uses of language models and the breadth of people they aim to serve.

Acknowledgements
This is joint work with Xuezhi Wang, Ian Tenney, Ellie Pavlick, Alex Beutel, Jilin Chen, Emily Pitler, and Slav Petrov. We benefited greatly throughout the project from discussions with Fernando Pereira, Ed Chi, Dipanjan Das, Vera Axelrod, Jacob Eisenstein, Tulsee Doshi, and James Wexler.



1 Zari is an Afghan Muppet designed to show that ‘a little girl could do as much as everybody else’.

Source: Google AI Blog


Measuring Gendered Correlations in Pre-trained NLP Models

Natural language processing (NLP) has seen significant progress over the past several years, with pre-trained models like BERT, ALBERT, ELECTRA, and XLNet achieving remarkable accuracy across a variety of tasks. In pre-training, representations are learned from a large text corpus, e.g., Wikipedia, by repeatedly masking out words and trying to predict them (this is called masked language modeling). The resulting representations encode rich information about language and correlations between concepts, such as surgeons and scalpels. There is then a second training stage, fine-tuning, in which the model uses task-specific training data to learn how to use the general pre-trained representations to do a concrete task, like classification. Given the broad adoption of these representations in many NLP tasks, it is crucial to understand the information encoded in them and how any learned correlations affect performance downstream, to ensure the application of these models aligns with our AI Principles.

In “Measuring and Reducing Gendered Correlations in Pre-trained Models” we perform a case study on BERT and its low-memory counterpart ALBERT, looking at correlations related to gender, and formulate a series of best practices for using pre-trained language models. We present experimental results over public model checkpoints and an academic task dataset to illustrate how the best practices apply, providing a foundation for exploring settings beyond the scope of this case study. We will soon release a series of checkpoints, Zari1, which reduce gendered correlations while maintaining state-of-the-art accuracy on standard NLP task metrics.

Measuring Correlations
To understand how correlations in pre-trained representations can affect downstream task performance, we apply a diverse set of evaluation metrics for studying the representation of gender. Here, we’ll discuss results from one of these tests, based on coreference resolution, which is the capability that allows models to understand the correct antecedent to a given pronoun in a sentence. For example, in the sentence that follows, the model should recognize his refers to the nurse, and not to the patient.

The standard academic formulation of the task is the OntoNotes test (Hovy et al., 2006), and we measure how accurate a model is at coreference resolution in a general setting using an F1 score over this data (as in Tenney et al. 2019). Since OntoNotes represents only one data distribution, we also consider the WinoGender benchmark that provides additional, balanced data designed to identify when model associations between gender and profession incorrectly influence coreference resolution. High values of the WinoGender metric (close to one) indicate a model is basing decisions on normative associations between gender and profession (e.g., associating nurse with the female gender and not male). When model decisions have no consistent association between gender and profession, the score is zero, which suggests that decisions are based on some other information, such as sentence structure or semantics.

BERT and ALBERT metrics on OntoNotes (accuracy) and WinoGender (gendered correlations). Low values on the WinoGender metric indicate that a model does not preferentially use gendered correlations in reasoning.

In this study, we see that neither the (Large) BERT or ALBERT public model achieves zero score on the WinoGender examples, despite achieving impressive accuracy on OntoNotes (close to 100%). At least some of this is due to models preferentially using gendered correlations in reasoning. This isn’t completely surprising: there are a range of cues available to understand text and it is possible for a general model to pick up on any or all of these. However, there is reason for caution, as it is undesirable for a model to make predictions primarily based on gendered correlations learned as priors rather than the evidence available in the input.

Best Practices
Given that it is possible for unintended correlations in pre-trained model representations to affect downstream task reasoning, we now ask: what can one do to mitigate any risk this poses when developing new NLP models?

  • It is important to measure for unintended correlations: Model quality may be assessed using accuracy metrics, but these only measure one dimension of performance, especially if the test data is drawn from the same distribution as the training data. For example, the BERT and ALBERT checkpoints have accuracy within 1% of each other, but differ by 26% (relative) in the degree to which they use gendered correlations for coreference resolution. This difference might be important for some tasks; selecting a model with low WinoGender score could be desirable in an application featuring texts about people in professions that may not conform to historical social norms, e.g., male nurses.
  • Be careful even when making seemingly innocuous configuration changes: Neural network model training is controlled by many hyperparameters that are usually selected to maximize some training objective. While configuration choices often seem innocuous, we find they can cause significant changes for gendered correlations, both for better and for worse. For example, dropout regularization is used to reduce overfitting by large models. When we increase the dropout rate used for pre-training BERT and ALBERT, we see a significant reduction in gendered correlations even after fine-tuning. This is promising since a simple configuration change allows us to train models with reduced risk of harm, but it also shows that we should be mindful and evaluate carefully when making any change in model configuration.
    Impact of increasing dropout regularization in BERT and ALBERT.
  • There are opportunities for general mitigations: A further corollary from the perhaps unexpected impact of dropout on gendered correlations is that it opens the possibility to use general-purpose methods for reducing unintended correlations: by increasing dropout in our study, we improve how the models reason about WinoGender examples without manually specifying anything about the task or changing the fine-tuning stage at all. Unfortunately, OntoNotes accuracy does start to decline as the dropout rate increases (which we can see in the BERT results), but we are excited about the potential to mitigate this in pre-training, where changes can lead to model improvements without the need for task-specific updates. We explore counterfactual data augmentation as another mitigation strategy with different tradeoffs in our paper.

What’s Next
We believe these best practices provide a starting point for developing robust NLP systems that perform well across the broadest possible range of linguistic settings and applications. Of course these techniques on their own are not sufficient to capture and remove all potential issues. Any model deployed in a real-world setting should undergo rigorous testing that considers the many ways it will be used, and implement safeguards to ensure alignment with ethical norms, such as Google's AI Principles. We look forward to developments in evaluation frameworks and data that are more expansive and inclusive to cover the many uses of language models and the breadth of people they aim to serve.

Acknowledgements
This is joint work with Xuezhi Wang, Ian Tenney, Ellie Pavlick, Alex Beutel, Jilin Chen, Emily Pitler, and Slav Petrov. We benefited greatly throughout the project from discussions with Fernando Pereira, Ed Chi, Dipanjan Das, Vera Axelrod, Jacob Eisenstein, Tulsee Doshi, and James Wexler.



1 Zari is an Afghan Muppet designed to show that ‘a little girl could do as much as everybody else’.

Source: Google AI Blog


Advancing NLP with Efficient Projection-Based Model Architectures

Deep neural networks have radically transformed natural language processing (NLP) in the last decade, primarily through their application in data centers using specialized hardware. However, issues such as preserving user privacy, eliminating network latency, enabling offline functionality, and reducing operation costs have rapidly spurred the development of NLP models that can be run on-device rather than in data centers. Yet mobile devices have limited memory and processing power, which requires models running on them to be small and efficient — without compromising quality.

Last year, we published a neural architecture called PRADO, which at the time achieved state-of-the-art performance on many text classification problems, using a model with less than 200K parameters. While most models use a fixed number of parameters per token, the PRADO model used a network structure that required extremely few parameters to learn the most relevant or useful tokens for the task.

Today we describe a new extension to the model, called pQRNN, which advances the state of the art for NLP performance with a minimal model size. The novelty of pQRNN is in how it combines a simple projection operation with a quasi-RNN encoder for fast, parallel processing. We show that the pQRNN model is able to achieve BERT-level performance on a text classification task with orders of magnitude fewer number of parameters.

What Makes PRADO Work?
When developed a year ago, PRADO exploited NLP domain-specific knowledge on text segmentation to reduce the model size and improve the performance. Normally, the text input to NLP models is first processed into a form that is suitable for the neural network, by segmenting text into pieces (tokens) that correspond to values in a predefined universal dictionary (a list of all possible tokens). The neural network then uniquely identifies each segment using a trainable parameter vector, which comprises the embedding table. However, the way in which text is segmented has a significant impact on the model performance, size, and latency. The figure below shows the spectrum of approaches used by the NLP community and their pros and cons.

Since the number of text segments is such an important parameter for model performance and compression, it raises the question of whether or not an NLP model needs to be able to distinctly identify every possible text segment. To answer this question we look at the inherent complexity of NLP tasks.

Only a few NLP tasks (e.g., language models and machine translation) need to know subtle differences between text segments and thus need to be capable of uniquely identifying all possible text segments. In contrast, the majority of other tasks can be solved by knowing a small subset of these segments. Furthermore, this subset of task-relevant segments will likely not be the most frequent, as a significant fraction of segments will undoubtedly be dedicated to articles, such as a, an, the, etc., which for many tasks are not necessarily critical. Hence, allowing the network to determine the most relevant segments for a given task results in better performance. In addition, the network does not need to be able to uniquely identify these segments, but only needs to recognize clusters of text segments. For example, a sentiment classifier just needs to know segment clusters that are strongly correlated to the sentiment in the text.

Leveraging these insights, PRADO was designed to learn clusters of text segments from words rather than word pieces or characters, which enabled it to achieve good performance on low-complexity NLP tasks. Since word units are more meaningful, and yet the most relevant words for most tasks are reasonably small, many fewer model parameters are needed to learn such a reduced subset of relevant word clusters.

Improving PRADO
Building on the success of PRADO, we developed an improved NLP model, called pQRNN. This model is composed of three building blocks, a projection operator that converts tokens in text to a sequence of ternary vectors, a dense bottleneck layer and a stack of QRNN encoders.

The implementation of the projection layer in pQRNN is identical to that used in PRADO and helps the model learn the most relevant tokens without a fixed set of parameters to define them. It first fingerprints the tokens in the text and converts it to a ternary feature vector using a simple mapping function. This results in a ternary vector sequence with a balanced symmetric distribution that uniquely represents the text. This representation is not directly useful since it does not have any information needed to solve the task of interest and the network has no control over this representation. We combine it with a dense bottleneck layer to allow the network to learn a per word representation that is relevant for the task at hand. The representation resulting from the bottleneck layer still does not take the context of the word into account. We learn a contextual representation by using a stack of bidirectional QRNN encoders. The result is a network that is capable of learning a contextual representation from just text input without employing any kind of preprocessing.

Performance
We evaluated pQRNN on the civil_comments dataset and compared it with the BERT model on the same task. Simply because the model size is proportional to the number of parameters, pQRNN is much smaller than BERT. But in addition, pQRNN is quantized, further reducing the model size by a factor of 4x. The public pretrained version of BERT performed poorly on the task hence the comparison is done to a BERT version that is pretrained on several different relevant multilingual data sources to achieve the best possible performance.

We capture the area under the curve (AUC) for the two models. Without any kind of pre-training and just trained on the supervised data, the AUC for pQRNN is 0.963 using 1.3 million quantized (8-bit) parameters. With pre-training on several different data sources and fine-tuning on the supervised data, the BERT model gets 0.976 AUC using 110 million floating point parameters.

Conclusion
Using our previous generation model PRADO, we have demonstrated how it can be used as the foundation for the next generation of state-of-the-art light-weight text classification models. We present one such model, pQRNN, and show that this new architecture can nearly achieve BERT-level performance, despite using 300x fewer parameters and being trained on only the supervised data. To stimulate further research in this area, we have open-sourced the PRADO model and encourage the community to use it as a jumping off point for new model architectures.

Acknowledgements
We thank Yicheng Fan, Márius Šajgalík, Peter Young and Arun Kandoor for contributing to the open sourcing effort and helping improve the models. We would also like to thank Amarnag Subramanya, Ashwini Venkatesh, Benoit Jacob, Catherine Wah, Dana Movshovitz-Attias, Dang Hien, Dmitry Kalenichenko, Edgar Gonzàlez i Pellicer, Edward Li, Erik Vee, Evgeny Livshits, Gaurav Nemade, Jeffrey Soren, Jeongwoo Ko, Julia Proskurnia, Rushin Shah, Shirin Badiezadegan, Sidharth KV, Victor Cărbune and the Learn2Compress team for their support. We would like to thank Andrew Tomkins and Patrick Mcgregor for sponsoring this research project.

Source: Google AI Blog


Advancing NLP with Efficient Projection-Based Model Architectures

Deep neural networks have radically transformed natural language processing (NLP) in the last decade, primarily through their application in data centers using specialized hardware. However, issues such as preserving user privacy, eliminating network latency, enabling offline functionality, and reducing operation costs have rapidly spurred the development of NLP models that can be run on-device rather than in data centers. Yet mobile devices have limited memory and processing power, which requires models running on them to be small and efficient — without compromising quality.

Last year, we published a neural architecture called PRADO, which at the time achieved state-of-the-art performance on many text classification problems, using a model with less than 200K parameters. While most models use a fixed number of parameters per token, the PRADO model used a network structure that required extremely few parameters to learn the most relevant or useful tokens for the task.

Today we describe a new extension to the model, called pQRNN, which advances the state of the art for NLP performance with a minimal model size. The novelty of pQRNN is in how it combines a simple projection operation with a quasi-RNN encoder for fast, parallel processing. We show that the pQRNN model is able to achieve BERT-level performance on a text classification task with orders of magnitude fewer number of parameters.

What Makes PRADO Work?
When developed a year ago, PRADO exploited NLP domain-specific knowledge on text segmentation to reduce the model size and improve the performance. Normally, the text input to NLP models is first processed into a form that is suitable for the neural network, by segmenting text into pieces (tokens) that correspond to values in a predefined universal dictionary (a list of all possible tokens). The neural network then uniquely identifies each segment using a trainable parameter vector, which comprises the embedding table. However, the way in which text is segmented has a significant impact on the model performance, size, and latency. The figure below shows the spectrum of approaches used by the NLP community and their pros and cons.

Since the number of text segments is such an important parameter for model performance and compression, it raises the question of whether or not an NLP model needs to be able to distinctly identify every possible text segment. To answer this question we look at the inherent complexity of NLP tasks.

Only a few NLP tasks (e.g., language models and machine translation) need to know subtle differences between text segments and thus need to be capable of uniquely identifying all possible text segments. In contrast, the majority of other tasks can be solved by knowing a small subset of these segments. Furthermore, this subset of task-relevant segments will likely not be the most frequent, as a significant fraction of segments will undoubtedly be dedicated to articles, such as a, an, the, etc., which for many tasks are not necessarily critical. Hence, allowing the network to determine the most relevant segments for a given task results in better performance. In addition, the network does not need to be able to uniquely identify these segments, but only needs to recognize clusters of text segments. For example, a sentiment classifier just needs to know segment clusters that are strongly correlated to the sentiment in the text.

Leveraging these insights, PRADO was designed to learn clusters of text segments from words rather than word pieces or characters, which enabled it to achieve good performance on low-complexity NLP tasks. Since word units are more meaningful, and yet the most relevant words for most tasks are reasonably small, many fewer model parameters are needed to learn such a reduced subset of relevant word clusters.

Improving PRADO
Building on the success of PRADO, we developed an improved NLP model, called pQRNN. This model is composed of three building blocks, a projection operator that converts tokens in text to a sequence of ternary vectors, a dense bottleneck layer and a stack of QRNN encoders.

The implementation of the projection layer in pQRNN is identical to that used in PRADO and helps the model learn the most relevant tokens without a fixed set of parameters to define them. It first fingerprints the tokens in the text and converts it to a ternary feature vector using a simple mapping function. This results in a ternary vector sequence with a balanced symmetric distribution that uniquely represents the text. This representation is not directly useful since it does not have any information needed to solve the task of interest and the network has no control over this representation. We combine it with a dense bottleneck layer to allow the network to learn a per word representation that is relevant for the task at hand. The representation resulting from the bottleneck layer still does not take the context of the word into account. We learn a contextual representation by using a stack of bidirectional QRNN encoders. The result is a network that is capable of learning a contextual representation from just text input without employing any kind of preprocessing.

Performance
We evaluated pQRNN on the civil_comments dataset and compared it with the BERT model on the same task. Simply because the model size is proportional to the number of parameters, pQRNN is much smaller than BERT. But in addition, pQRNN is quantized, further reducing the model size by a factor of 4x. The public pretrained version of BERT performed poorly on the task hence the comparison is done to a BERT version that is pretrained on several different relevant multilingual data sources to achieve the best possible performance.

We capture the area under the curve (AUC) for the two models. Without any kind of pre-training and just trained on the supervised data, the AUC for pQRNN is 0.963 using 1.3 million quantized (8-bit) parameters. With pre-training on several different data sources and fine-tuning on the supervised data, the BERT model gets 0.976 AUC using 110 million floating point parameters.

Conclusion
Using our previous generation model PRADO, we have demonstrated how it can be used as the foundation for the next generation of state-of-the-art light-weight text classification models. We present one such model, pQRNN, and show that this new architecture can nearly achieve BERT-level performance, despite using 300x fewer parameters and being trained on only the supervised data. To stimulate further research in this area, we have open-sourced the PRADO model and encourage the community to use it as a jumping off point for new model architectures.

Acknowledgements
We thank Yicheng Fan, Márius Šajgalík, Peter Young and Arun Kandoor for contributing to the open sourcing effort and helping improve the models. We would also like to thank Amarnag Subramanya, Ashwini Venkatesh, Benoit Jacob, Catherine Wah, Dana Movshovitz-Attias, Dang Hien, Dmitry Kalenichenko, Edgar Gonzàlez i Pellicer, Edward Li, Erik Vee, Evgeny Livshits, Gaurav Nemade, Jeffrey Soren, Jeongwoo Ko, Julia Proskurnia, Rushin Shah, Shirin Badiezadegan, Sidharth KV, Victor Cărbune and the Learn2Compress team for their support. We would like to thank Andrew Tomkins and Patrick Mcgregor for sponsoring this research project.

Source: Google AI Blog


Advancing NLP with Efficient Projection-Based Model Architectures

Deep neural networks have radically transformed natural language processing (NLP) in the last decade, primarily through their application in data centers using specialized hardware. However, issues such as preserving user privacy, eliminating network latency, enabling offline functionality, and reducing operation costs have rapidly spurred the development of NLP models that can be run on-device rather than in data centers. Yet mobile devices have limited memory and processing power, which requires models running on them to be small and efficient — without compromising quality.

Last year, we published a neural architecture called PRADO, which at the time achieved state-of-the-art performance on many text classification problems, using a model with less than 200K parameters. While most models use a fixed number of parameters per token, the PRADO model used a network structure that required extremely few parameters to learn the most relevant or useful tokens for the task.

Today we describe a new extension to the model, called pQRNN, which advances the state of the art for NLP performance with a minimal model size. The novelty of pQRNN is in how it combines a simple projection operation with a quasi-RNN encoder for fast, parallel processing. We show that the pQRNN model is able to achieve BERT-level performance on a text classification task with orders of magnitude fewer number of parameters.

What Makes PRADO Work?
When developed a year ago, PRADO exploited NLP domain-specific knowledge on text segmentation to reduce the model size and improve the performance. Normally, the text input to NLP models is first processed into a form that is suitable for the neural network, by segmenting text into pieces (tokens) that correspond to values in a predefined universal dictionary (a list of all possible tokens). The neural network then uniquely identifies each segment using a trainable parameter vector, which comprises the embedding table. However, the way in which text is segmented has a significant impact on the model performance, size, and latency. The figure below shows the spectrum of approaches used by the NLP community and their pros and cons.

Since the number of text segments is such an important parameter for model performance and compression, it raises the question of whether or not an NLP model needs to be able to distinctly identify every possible text segment. To answer this question we look at the inherent complexity of NLP tasks.

Only a few NLP tasks (e.g., language models and machine translation) need to know subtle differences between text segments and thus need to be capable of uniquely identifying all possible text segments. In contrast, the majority of other tasks can be solved by knowing a small subset of these segments. Furthermore, this subset of task-relevant segments will likely not be the most frequent, as a significant fraction of segments will undoubtedly be dedicated to articles, such as a, an, the, etc., which for many tasks are not necessarily critical. Hence, allowing the network to determine the most relevant segments for a given task results in better performance. In addition, the network does not need to be able to uniquely identify these segments, but only needs to recognize clusters of text segments. For example, a sentiment classifier just needs to know segment clusters that are strongly correlated to the sentiment in the text.

Leveraging these insights, PRADO was designed to learn clusters of text segments from words rather than word pieces or characters, which enabled it to achieve good performance on low-complexity NLP tasks. Since word units are more meaningful, and yet the most relevant words for most tasks are reasonably small, many fewer model parameters are needed to learn such a reduced subset of relevant word clusters.

Improving PRADO
Building on the success of PRADO, we developed an improved NLP model, called pQRNN. This model is composed of three building blocks, a projection operator that converts tokens in text to a sequence of ternary vectors, a dense bottleneck layer and a stack of QRNN encoders.

The implementation of the projection layer in pQRNN is identical to that used in PRADO and helps the model learn the most relevant tokens without a fixed set of parameters to define them. It first fingerprints the tokens in the text and converts it to a ternary feature vector using a simple mapping function. This results in a ternary vector sequence with a balanced symmetric distribution that uniquely represents the text. This representation is not directly useful since it does not have any information needed to solve the task of interest and the network has no control over this representation. We combine it with a dense bottleneck layer to allow the network to learn a per word representation that is relevant for the task at hand. The representation resulting from the bottleneck layer still does not take the context of the word into account. We learn a contextual representation by using a stack of bidirectional QRNN encoders. The result is a network that is capable of learning a contextual representation from just text input without employing any kind of preprocessing.

Performance
We evaluated pQRNN on the civil_comments dataset and compared it with the BERT model on the same task. Simply because the model size is proportional to the number of parameters, pQRNN is much smaller than BERT. But in addition, pQRNN is quantized, further reducing the model size by a factor of 4x. The public pretrained version of BERT performed poorly on the task hence the comparison is done to a BERT version that is pretrained on several different relevant multilingual data sources to achieve the best possible performance.

We capture the area under the curve (AUC) for the two models. Without any kind of pre-training and just trained on the supervised data, the AUC for pQRNN is 0.963 using 1.3 million quantized (8-bit) parameters. With pre-training on several different data sources and fine-tuning on the supervised data, the BERT model gets 0.976 AUC using 110 million floating point parameters.

Conclusion
Using our previous generation model PRADO, we have demonstrated how it can be used as the foundation for the next generation of state-of-the-art light-weight text classification models. We present one such model, pQRNN, and show that this new architecture can nearly achieve BERT-level performance, despite using 300x fewer parameters and being trained on only the supervised data. To stimulate further research in this area, we have open-sourced the PRADO model and encourage the community to use it as a jumping off point for new model architectures.

Acknowledgements
We thank Yicheng Fan, Márius Šajgalík, Peter Young and Arun Kandoor for contributing to the open sourcing effort and helping improve the models. We would also like to thank Amarnag Subramanya, Ashwini Venkatesh, Benoit Jacob, Catherine Wah, Dana Movshovitz-Attias, Dang Hien, Dmitry Kalenichenko, Edgar Gonzàlez i Pellicer, Edward Li, Erik Vee, Evgeny Livshits, Gaurav Nemade, Jeffrey Soren, Jeongwoo Ko, Julia Proskurnia, Rushin Shah, Shirin Badiezadegan, Sidharth KV, Victor Cărbune and the Learn2Compress team for their support. We would like to thank Andrew Tomkins and Patrick Mcgregor for sponsoring this research project.

Source: Google AI Blog