Tag Archives: EMNLP

Constructing Transformers For Longer Sequences with Sparse Attention Methods

Natural language processing (NLP) models based on Transformers, such as BERT, RoBERTa, T5, or GPT3, are successful for a wide variety of tasks and a mainstay of modern NLP research. The versatility and robustness of Transformers are the primary drivers behind their wide-scale adoption, leading them to be easily adapted for a diverse range of sequence-based tasks — as a seq2seq model for translation, summarization, generation, and others, or as a standalone encoder for sentiment analysis, POS tagging, machine reading comprehension, etc. The key innovation in Transformers is the introduction of a self-attention mechanism, which computes similarity scores for all pairs of positions in an input sequence, and can be evaluated in parallel for each token of the input sequence, avoiding the sequential dependency of recurrent neural networks, and enabling Transformers to vastly outperform previous sequence models like LSTM.

A limitation of existing Transformer models and their derivatives, however, is that the full self-attention mechanism has computational and memory requirements that are quadratic with the input sequence length. With commonly available current hardware and model sizes, this typically limits the input sequence to roughly 512 tokens, and prevents Transformers from being directly applicable to tasks that require larger context, like question answering, document summarization or genome fragment classification. Two natural questions arise: 1) Can we achieve the empirical benefits of quadratic full Transformers using sparse models with computational and memory requirements that scale linearly with the input sequence length? 2) Is it possible to show theoretically that these linear Transformers preserve the expressivity and flexibility of the quadratic full Transformers?

We address both of these questions in a recent pair of papers. In “ETC: Encoding Long and Structured Inputs in Transformers”, presented at EMNLP 2020, we present the Extended Transformer Construction (ETC), which is a novel method for sparse attention, in which one uses structural information to limit the number of computed pairs of similarity scores. This reduces the quadratic dependency on input length to linear and yields strong empirical results in the NLP domain. Then, in “Big Bird: Transformers for Longer Sequences”, presented at NeurIPS 2020, we introduce another sparse attention method, called BigBird that extends ETC to more generic scenarios where prerequisite domain knowledge about structure present in the source data may be unavailable. Moreover, we also show that theoretically our proposed sparse attention mechanism preserves the expressivity and flexibility of the quadratic full Transformers. Our proposed methods achieve a new state of the art on challenging long-sequence tasks, including question answering, document summarization and genome fragment classification.

Attention as a Graph
The attention module used in Transformer models computes similarity scores for all pairs of positions in an input sequence. It is useful to think of the attention mechanism as a directed graph, with tokens represented by nodes and the similarity score computed between a pair of tokens represented by an edge. In this view, the full attention model is a complete graph. The core idea behind our approach is to carefully design sparse graphs, such that one only computes a linear number of similarity scores.

Full attention can be viewed as a complete graph.

Extended Transformer Construction (ETC)
On NLP tasks that require long and structured inputs, we propose a structured sparse attention mechanism, which we call Extended Transformer Construction (ETC). To achieve structured sparsification of self attention, we developed the global-local attention mechanism. Here the input to the Transformer is split into two parts: a global input where tokens have unrestricted attention, and a long input where tokens can only attend to either the global input or to a local neighborhood. This achieves linear scaling of attention, which allows ETC to significantly scale input length.

In order to further exploit the structure of long documents, ETC combines additional ideas: representing the positional information of the tokens in a relative way, rather than using their absolute position in the sequence; using an additional training objective beyond the usual masked language model (MLM) used in models like BERT; and flexible masking of tokens to control which tokens can attend to which other tokens. For example, given a long selection of text, a global token is applied to each sentence, which connects to all tokens within the sentence, and a global token is also applied to each paragraph, which connects to all tokens within the same paragraph.

An example of document structure based sparse attention of ETC model. The global variables are denoted by C (in blue) for paragraph, S (yellow) for sentence while the local variables are denoted by X (grey) for tokens corresponding to the long input.

With this approach, we report state-of-the-art results in five challenging NLP datasets requiring long or structured inputs: TriviaQA, Natural Questions (NQ), HotpotQA, WikiHop, and OpenKP.

Test set result on Question Answering. For both verified TriviaQA and WikiHop, using ETC achieved a new state of the art.

BigBird
Extending the work of ETC, we propose BigBird — a sparse attention mechanism that is also linear in the number of tokens and is a generic replacement for the attention mechanism used in Transformers. In contrast to ETC, BigBird doesn’t require any prerequisite knowledge about structure present in the source data. Sparse attention in the BigBird model consists of three main parts:

  • A set of global tokens attending to all parts of the input sequence
  • All tokens attending to a set of local neighboring tokens
  • All tokens attending to a set of random tokens
BigBird sparse attention can be seen as adding few global tokens on Watts-Strogatz graph.

In the BigBird paper, we explain why sparse attention is sufficient to approximate quadratic attention, partially explaining why ETC was successful. A crucial observation is that there is an inherent tension between how few similarity scores one computes and the flow of information between different nodes (i.e., the ability of one token to influence each other). Global tokens serve as a conduit for information flow and we prove that sparse attention mechanisms with global tokens can be as powerful as the full attention model. In particular, we show that BigBird is as expressive as the original Transformer, is computationally universal (following the work of Yun et al. and Perez et al.), and is a universal approximator of continuous functions. Furthermore, our proof suggests that the use of random graphs can further help ease the flow of information — motivating the use of the random attention component.

This design scales to much longer sequence lengths for both structured and unstructured tasks. Further scaling can be achieved by using gradient checkpointing by trading off training time for sequence length. This lets us extend our efficient sparse transformers to include generative tasks that require an encoder and a decoder, such as long document summarization, on which we achieve a new state of the art.

Summarization ROUGE score for long documents. Both for BigPatent and ArXiv datasets, we achieve a new state of the art result.

Moreover, the fact that BigBird is a generic replacement also allows it to be extended to new domains without pre-existing domain knowledge. In particular, we introduce a novel application of Transformer-based models where long contexts are beneficial — extracting contextual representations of genomic sequences (DNA). With longer masked language model pre-training, BigBird achieves state-of-the-art performance on downstream tasks, such as promoter-region prediction and chromatin profile prediction.

On multiple genomics tasks, such as promoter region prediction (PRP), chromatin-profile prediction including transcription factors (TF), histone-mark (HM) and DNase I hypersensitive (DHS) detection, we outperform baselines. Moreover our results show that Transformer models can be applied to multiple genomics tasks that are currently underexplored.

Main Implementation Idea
One of the main impediments to the large scale adoption of sparse attention is the fact that sparse operations are quite inefficient in modern hardware. Behind both ETC and BigBird, one of our key innovations is to make an efficient implementation of the sparse attention mechanism. As modern hardware accelerators like GPUs and TPUs excel using coalesced memory operations, which load blocks of contiguous bytes at once, it is not efficient to have small sporadic look-ups caused by a sliding window (for local attention) or random element queries (random attention). Instead we transform the sparse local and random attention into dense tensor operations to take full advantage of modern single instruction, multiple data (SIMD) hardware.

To do this, we first “blockify” the attention mechanism to better leverage GPUs/TPUs, which are designed to operate on blocks. Then we convert the sparse attention mechanism computation into a dense tensor product through a series of simple matrix operations such as reshape, roll, and gather, as illustrated in the animation below.

Illustration of how sparse window attention is efficiently computed using roll and reshape, and without small sporadic look-ups.

Recently, “Long Range Arena: A Benchmark for Efficient Transformers“ provided a benchmark of six tasks that require longer context, and performed experiments to benchmark all existing long range transformers. The results show that the BigBird model, unlike its counterparts, clearly reduces memory consumption without sacrificing performance.

Conclusion
We show that carefully designed sparse attention can be as expressive and flexible as the original full attention model. Along with theoretical guarantees, we provide a very efficient implementation which allows us to scale to much longer inputs. As a consequence, we achieve state-of-the-art results for question answering, document summarization and genome fragment classification. Given the generic nature of our sparse attention, the approach should be applicable to many other tasks like program synthesis and long form open domain question answering. We have open sourced the code for both ETC (github) and BigBird (github), both of which run efficiently for long sequences on both GPUs and TPUs.

Acknowledgements
This research resulted as a collaboration with Amr Ahmed, Joshua Ainslie, Chris Alberti, Vaclav Cvicek, Avinava Dubey, Zachary Fisher, Guru Guruganesh, Santiago Ontañón, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang, Manzil Zaheer, who co-authored EMNLP and NeurIPS papers.

Source: Google AI Blog


Constructing Transformers For Longer Sequences with Sparse Attention Methods

Natural language processing (NLP) models based on Transformers, such as BERT, RoBERTa, T5, or GPT3, are successful for a wide variety of tasks and a mainstay of modern NLP research. The versatility and robustness of Transformers are the primary drivers behind their wide-scale adoption, leading them to be easily adapted for a diverse range of sequence-based tasks — as a seq2seq model for translation, summarization, generation, and others, or as a standalone encoder for sentiment analysis, POS tagging, machine reading comprehension, etc. The key innovation in Transformers is the introduction of a self-attention mechanism, which computes similarity scores for all pairs of positions in an input sequence, and can be evaluated in parallel for each token of the input sequence, avoiding the sequential dependency of recurrent neural networks, and enabling Transformers to vastly outperform previous sequence models like LSTM.

A limitation of existing Transformer models and their derivatives, however, is that the full self-attention mechanism has computational and memory requirements that are quadratic with the input sequence length. With commonly available current hardware and model sizes, this typically limits the input sequence to roughly 512 tokens, and prevents Transformers from being directly applicable to tasks that require larger context, like question answering, document summarization or genome fragment classification. Two natural questions arise: 1) Can we achieve the empirical benefits of quadratic full Transformers using sparse models with computational and memory requirements that scale linearly with the input sequence length? 2) Is it possible to show theoretically that these linear Transformers preserve the expressivity and flexibility of the quadratic full Transformers?

We address both of these questions in a recent pair of papers. In “ETC: Encoding Long and Structured Inputs in Transformers”, presented at EMNLP 2020, we present the Extended Transformer Construction (ETC), which is a novel method for sparse attention, in which one uses structural information to limit the number of computed pairs of similarity scores. This reduces the quadratic dependency on input length to linear and yields strong empirical results in the NLP domain. Then, in “Big Bird: Transformers for Longer Sequences”, presented at NeurIPS 2020, we introduce another sparse attention method, called BigBird that extends ETC to more generic scenarios where prerequisite domain knowledge about structure present in the source data may be unavailable. Moreover, we also show that theoretically our proposed sparse attention mechanism preserves the expressivity and flexibility of the quadratic full Transformers. Our proposed methods achieve a new state of the art on challenging long-sequence tasks, including question answering, document summarization and genome fragment classification.

Attention as a Graph
The attention module used in Transformer models computes similarity scores for all pairs of positions in an input sequence. It is useful to think of the attention mechanism as a directed graph, with tokens represented by nodes and the similarity score computed between a pair of tokens represented by an edge. In this view, the full attention model is a complete graph. The core idea behind our approach is to carefully design sparse graphs, such that one only computes a linear number of similarity scores.

Full attention can be viewed as a complete graph.

Extended Transformer Construction (ETC)
On NLP tasks that require long and structured inputs, we propose a structured sparse attention mechanism, which we call Extended Transformer Construction (ETC). To achieve structured sparsification of self attention, we developed the global-local attention mechanism. Here the input to the Transformer is split into two parts: a global input where tokens have unrestricted attention, and a long input where tokens can only attend to either the global input or to a local neighborhood. This achieves linear scaling of attention, which allows ETC to significantly scale input length.

In order to further exploit the structure of long documents, ETC combines additional ideas: representing the positional information of the tokens in a relative way, rather than using their absolute position in the sequence; using an additional training objective beyond the usual masked language model (MLM) used in models like BERT; and flexible masking of tokens to control which tokens can attend to which other tokens. For example, given a long selection of text, a global token is applied to each sentence, which connects to all tokens within the sentence, and a global token is also applied to each paragraph, which connects to all tokens within the same paragraph.

An example of document structure based sparse attention of ETC model. The global variables are denoted by C (in blue) for paragraph, S (yellow) for sentence while the local variables are denoted by X (grey) for tokens corresponding to the long input.

With this approach, we report state-of-the-art results in five challenging NLP datasets requiring long or structured inputs: TriviaQA, Natural Questions (NQ), HotpotQA, WikiHop, and OpenKP.

Test set result on Question Answering. For both verified TriviaQA and WikiHop, using ETC achieved a new state of the art.

BigBird
Extending the work of ETC, we propose BigBird — a sparse attention mechanism that is also linear in the number of tokens and is a generic replacement for the attention mechanism used in Transformers. In contrast to ETC, BigBird doesn’t require any prerequisite knowledge about structure present in the source data. Sparse attention in the BigBird model consists of three main parts:

  • A set of global tokens attending to all parts of the input sequence
  • All tokens attending to a set of local neighboring tokens
  • All tokens attending to a set of random tokens
BigBird sparse attention can be seen as adding few global tokens on Watts-Strogatz graph.

In the BigBird paper, we explain why sparse attention is sufficient to approximate quadratic attention, partially explaining why ETC was successful. A crucial observation is that there is an inherent tension between how few similarity scores one computes and the flow of information between different nodes (i.e., the ability of one token to influence each other). Global tokens serve as a conduit for information flow and we prove that sparse attention mechanisms with global tokens can be as powerful as the full attention model. In particular, we show that BigBird is as expressive as the original Transformer, is computationally universal (following the work of Yun et al. and Perez et al.), and is a universal approximator of continuous functions. Furthermore, our proof suggests that the use of random graphs can further help ease the flow of information — motivating the use of the random attention component.

This design scales to much longer sequence lengths for both structured and unstructured tasks. Further scaling can be achieved by using gradient checkpointing by trading off training time for sequence length. This lets us extend our efficient sparse transformers to include generative tasks that require an encoder and a decoder, such as long document summarization, on which we achieve a new state of the art.

Summarization ROUGE score for long documents. Both for BigPatent and ArXiv datasets, we achieve a new state of the art result.

Moreover, the fact that BigBird is a generic replacement also allows it to be extended to new domains without pre-existing domain knowledge. In particular, we introduce a novel application of Transformer-based models where long contexts are beneficial — extracting contextual representations of genomic sequences (DNA). With longer masked language model pre-training, BigBird achieves state-of-the-art performance on downstream tasks, such as promoter-region prediction and chromatin profile prediction.

On multiple genomics tasks, such as promoter region prediction (PRP), chromatin-profile prediction including transcription factors (TF), histone-mark (HM) and DNase I hypersensitive (DHS) detection, we outperform baselines. Moreover our results show that Transformer models can be applied to multiple genomics tasks that are currently underexplored.

Main Implementation Idea
One of the main impediments to the large scale adoption of sparse attention is the fact that sparse operations are quite inefficient in modern hardware. Behind both ETC and BigBird, one of our key innovations is to make an efficient implementation of the sparse attention mechanism. As modern hardware accelerators like GPUs and TPUs excel using coalesced memory operations, which load blocks of contiguous bytes at once, it is not efficient to have small sporadic look-ups caused by a sliding window (for local attention) or random element queries (random attention). Instead we transform the sparse local and random attention into dense tensor operations to take full advantage of modern single instruction, multiple data (SIMD) hardware.

To do this, we first “blockify” the attention mechanism to better leverage GPUs/TPUs, which are designed to operate on blocks. Then we convert the sparse attention mechanism computation into a dense tensor product through a series of simple matrix operations such as reshape, roll, and gather, as illustrated in the animation below.

Illustration of how sparse window attention is efficiently computed using roll and reshape, and without small sporadic look-ups.

Recently, “Long Range Arena: A Benchmark for Efficient Transformers“ provided a benchmark of six tasks that require longer context, and performed experiments to benchmark all existing long range transformers. The results show that the BigBird model, unlike its counterparts, clearly reduces memory consumption without sacrificing performance.

Conclusion
We show that carefully designed sparse attention can be as expressive and flexible as the original full attention model. Along with theoretical guarantees, we provide a very efficient implementation which allows us to scale to much longer inputs. As a consequence, we achieve state-of-the-art results for question answering, document summarization and genome fragment classification. Given the generic nature of our sparse attention, the approach should be applicable to many other tasks like program synthesis and long form open domain question answering. We have open sourced the code for both ETC (github) and BigBird (github), both of which run efficiently for long sequences on both GPUs and TPUs.

Acknowledgements
This research resulted as a collaboration with Amr Ahmed, Joshua Ainslie, Chris Alberti, Vaclav Cvicek, Avinava Dubey, Zachary Fisher, Guru Guruganesh, Santiago Ontañón, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang, Manzil Zaheer, who co-authored EMNLP and NeurIPS papers.

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


ToTTo: A Controlled Table-to-Text Generation Dataset

In the last few years, research in natural language generation, used for tasks like text summarization, has made tremendous progress. Yet, despite achieving high levels of fluency, neural systems can still be prone to hallucination (i.e.generating text that is understandable, but not faithful to the source), which can prohibit these systems from being used in many applications that require high degrees of accuracy. Consider an example from the Wikibio dataset, where the neural baseline model tasked with summarizing a Wikipedia infobox entry for Belgian football player Constant Vanden Stock summarizes incorrectly that he is an American figure skater.

While the process of assessing the faithfulness of generated text to the source content can be challenging, it is often easier when the source content is structured (e.g., in tabular format). Moreover, structured data can also test a model’s ability for reasoning and numerical inference. However, existing large scale structured datasets are often noisy (i.e., the reference sentence cannot be fully inferred from the tabular data), making them unreliable for the measurement of hallucination in model development.

In “ToTTo: A Controlled Table-To-Text Generation Dataset”, we present an open domain table-to-text generation dataset created using a novel annotation process (via sentence revision) along with a controlled text generation task that can be used to assess model hallucination. ToTTo (shorthand for “Table-To-Text”) consists of 121,000 training examples, along with 7,500 examples each for development and test. Due to the accuracy of annotations, this dataset is suitable as a challenging benchmark for research in high precision text generation. The dataset and code are open-sourced on our GitHub repo.

Table-to-Text Generation
ToTTo introduces a controlled generation task in which a given Wikipedia table with a set of selected cells is used as the source material for the task of producing a single sentence description that summarizes the cell contents in the context of the table. The example below demonstrates some of the many challenges posed by the task, such as numerical reasoning, a large open-domain vocabulary, and varied table structure.

Example in the ToTTo dataset, where given the source table and set of highlighted cells (left), the goal is to generate a one sentence description, such as the “target sentence” (right). Note that generating the target sentence would require numerical inference (eleven NFL seasons) and understanding of the NFL domain.

Annotation Process
Designing an annotation process to obtain natural but also clean target sentences from tabular data is a significant challenge. Many datasets like Wikibio and RotoWire pair naturally occurring text heuristically with tables, a noisy process that makes it difficult to disentangle whether hallucination is primarily caused by data noise or model shortcomings. On the other hand, one can elicit annotators to write sentence targets from scratch, which are faithful to the table, but the resulting targets often lack variety in terms of structure and style.

In contrast, ToTTo is constructed using a novel data annotation strategy in which annotators revise existing Wikipedia sentences in stages. This results in target sentences that are clean, as well as natural, containing interesting and varied linguistic properties. The data collection and annotation process begins by collecting tables from Wikipedia, where a given table is paired with a summary sentence collected from the supporting page context according to heuristics, such as word overlap between the page text and the table and hyperlinks referencing tabular data. This summary sentence may contain information not supported by the table and may contain pronouns with antecedents found in the table only, not the sentence itself.

The annotator then highlights the cells in the table that support the sentence and deletes phrases in the sentence that are not supported by the table. They also decontextualize the sentence so that it is standalone (e.g., with correct pronoun resolution) and correct grammar, where necessary.

We show that annotators obtain high agreement on the above task: 0.856 Fleiss Kappa for cell highlighting, and 67.0 BLEU for the final target sentence.

Dataset Analysis
We conducted a topic analysis on the ToTTo dataset over 44 categories and found that the Sports and Countries topics, each of which consists of a range of fine-grained topics, e.g., football/olympics for sports and population/buildings for countries, together comprise 56.4% of the dataset. The other 44% is composed of a much more broad set of topics, including Performing Arts, Transportation, and Entertainment.

Furthermore, we conducted a manual analysis of the different types of linguistic phenomena in the dataset over 100 randomly chosen examples. The table below summarizes the fraction of examples that require reference to the page and section titles, as well as some of the linguistic phenomena in the dataset that potentially pose new challenges to current systems.

Linguistic Phenomena Percentage
Require reference to page title 82%
Require reference to section title 19%
Require reference to table description 3%
Reasoning (logical, numerical, temporal etc.) 21%
Comparison across rows/columns/cells 13%
Require background information 12%

Baseline Results
We present some baseline results of three state-of-the-art models from the literature (BERT-to-BERT, Pointer Generator, and the Puduppully 2019 model) on two evaluation metrics, BLEU and PARENT. In addition to reporting the score on the overall test set, we also evaluate each model on a more challenging subset consisting of out-of-domain examples. As the table below shows, the BERT-to-BERT model performs best in terms of both BLEU and PARENT. Moreover, all models achieve considerably lower performance on the challenge set indicating the challenge of out-of-domain generalization.

  BLEU PARENT BLEU PARENT
Model (overall) (overall) (challenge) (challenge)
BERT-to-BERT 43.9 52.6 34.8 46.7
Pointer Generator 41.6 51.6 32.2 45.2
Puduppully et al. 2019 19.2 29.2 13.9 25.8

While automatic metrics can give some indication of performance, they are not currently sufficient for evaluating hallucination in text generation systems. To better understand hallucination, we manually evaluate the top performing baseline, to determine how faithful it is to the content in the source table, under the assumption that discrepancies indicate hallucination. To compute the “Expert” performance, for each example in our multi-reference test set, we held out one reference and asked annotators to compare it with the other references for faithfulness. As the results show, the top performing baseline appears to hallucinate information ~20% of the time.

  Faithfulness Faithfulness
Model (overall) (challenge)
Expert 93.6 91.4
BERT-to-BERT  76.2 74.2

Model Errors and Challenges
In the table below, we present a selection of the observed model errors to highlight some of the more challenging aspects of the ToTTo dataset. We find that state-of-the-art models struggle with hallucination, numerical reasoning, and rare topics, even when using cleaned references (errors in red). The last example shows that even when the model output is correct it is sometimes not as informative as the original reference which contains more reasoning about the table (shown in blue).

Reference Model Prediction
in the 1939 currie cup, western province lost to transvaal by 17–6 in cape town. the first currie cup was played in 1939 in transvaal1 at new- lands, with western province winning 17–6.
a second generation of micro- drive was announced by ibm in 2000 with increased capacities at 512 mb and 1 gb. there were 512 microdrive models in 2000: 1 gigabyte.
the 1956 grand prix motorcy- cle racing season consisted of six grand prix races in five classes: 500cc, 350cc, 250cc, 125cc and sidecars 500cc. the 1956 grand prix motorcycle racing season consisted of eight grand prix races in five classes: 500cc, 350cc, 250cc, 125cc and sidecars 500cc.
in travis kelce’s last collegiate season, he set personal career highs in receptions (45), re- ceiving yards (722), yards per receptions (16.0) and receiving touchdowns (8). travis kelce finished the 2012 season with 45 receptions for 722 yards (16.0 avg.) and eight touchdowns.

Conclusion
In this work, we presented ToTTo, a large, English table-to-text dataset that presents both a controlled generation task and a data annotation process based on iterative sentence revision. We also provided several state-of-the-art baselines, and demonstrated ToTTo could be a useful dataset for modeling research as well as for developing evaluation metrics that can better detect model improvements.

In addition to the proposed task, we hope our dataset can also be helpful for other tasks such as table understanding and sentence revision. ToTTo is available at our GitHub repo.

Acknowledgements
The authors wish to thank Ming-Wei Chang, Jonathan H. Clark, Kenton Lee, and Jennimaria Palomaki for their insightful discussions and support. Many thanks also to Ashwin Kakarla and his team for help with the annotations.

Source: Google AI Blog


Encode, Tag and Realize: A Controllable and Efficient Approach for Text Generation



Sequence-to-sequence (seq2seq) models have revolutionized the field of machine translation and have become the tool of choice for various text-generation tasks, such as summarization, sentence fusion and grammatical error correction. Improvements in model architecture (e.g., Transformer) and the ability to leverage large corpora of unannotated text via unsupervised pre-training have enabled the quality gains in neural network approaches we have seen in recent years.

Yet, the use of seq2seq models for text generation can come with a number of substantial drawbacks depending on the use case, such as producing outputs that are not supported by the input text (known as hallucination) and requiring large amounts of training data to reach good performance. Furthermore, seq2seq models are inherently slow at inference time, since they typically generate the output word-by-word.

In “Encode, Tag, Realize: High-Precision Text Editing,” we present a novel, open sourced method for text generation, which is designed to specifically address these three shortcomings. This method is called LaserTagger, owing to the speed and precision of the method. Instead of generating the output text from scratch, LaserTagger produces output by tagging words with predicted edit operations that are then applied to the input words in a separate realization step. This is a less error-prone way of tackling text generation, which can be handled by an easier to train and faster to execute model architecture.

Design and Functionality of LaserTagger
A distinct characteristic of many text-generation tasks is that there is often a high overlap between the input and the output. For instance, when detecting and fixing grammatical mistakes or when fusing sentences, most of the input text can remain unchanged, and only a small fraction of the words needs to be modified. For this reason, LaserTagger produces a sequence of edit operations instead of actual words. The four types of edit operations we use are: Keep (copies a word to the output), Delete (removes a word) and Keep-AddX / Delete-AddX (adds phrase X before the tagged word and optionally deletes the tagged word). This process is illustrated in the figure below, which shows an application of LaserTagger to sentence fusion.
LaserTagger applied to sentence fusion. The predicted edit operations correspond to deleting “. Turing" and adding "and he" before it. Notice the high overlap between the input and output text.
All added phrases come from a restricted vocabulary. This vocabulary is the result of an optimization process that has two goals: (1) minimizing the vocabulary size and (2) maximizing the number of training examples, where the only words necessary to add to the target text come from the vocabulary alone. Having a restricted phrase vocabulary makes the space of output decisions smaller and prevents the model from adding arbitrary words, hence mitigating the problem of hallucination. A corollary of the high-overlap property of input and output texts is that required modifications tend to be local and independent from one another. This means that the edit operations can be predicted in parallel with high accuracy, enabling a significant end-to-end speed up compared to autoregressive seq2seq models, which perform the predictions sequentially, conditioning on the previous predictions.

Results
We evaluated LaserTagger on four tasks: sentence fusion, split and rephrase, abstractive summarization, and grammar correction. Across the tasks, LaserTagger performs comparably to a strong BERT-based seq2seq baseline that uses a large number of training examples, and clearly outperforms this baseline when the number of training examples is limited. Below we show the results on the WikiSplit dataset, where the task is to rephrase a long sentence into two coherent short sentences.
When training the models on the full dataset of 1 million examples, both LaserTagger and a BERT-based seq2seq baseline model perform comparably, but when training on a subsample of 10,000 examples or less, LaserTagger clearly outperforms the baseline model (the higher the SARI score the better).
Key Advantages of LaserTagger
Compared to traditional seq2seq methods, LaserTagger has the following advantages:
  1. Control: By controlling the output phrase vocabulary, which we can also manually edit or curate, LaserTagger is less susceptible to hallucination than the seq2seq baseline.
  2. Inference speed: LaserTagger computes predictions up to 100 times faster than the seq2seq baseline, making it suitable for real-time applications.
  3. Data efficiency: LaserTagger produces reasonable outputs, even when trained using only a few hundred or a few thousand training examples. In our experiments, a competitive seq2seq baseline required tens of thousands of examples to obtain comparable performance.
Why This Matters
The advantages of LaserTagger become even more pronounced when applied at large scale, such as improving the formulation of voice answers in some services by reducing the length of the responses and making them less repetitive. The high inference speed allows the model to be plugged into an existing technology stack, without adding any noticeable latency on the user side, while the improved data efficiency enables the collection of training data for many languages, thus benefiting users from different language backgrounds.

In our current work, we strive to bring similar improvements to other Google technologies that produce natural language. Furthermore, we are exploring how the editing of texts (instead of their generation from scratch) can help us to better understand user queries as they grow longer, become more complex, and come as part of a dialogue discourse. The code for LaserTagger is open-sourced to the community through our GitHub repo.

Acknowledgements
This research was conducted by Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. We are grateful for useful discussions with Enrique Alfonseca, Idan Szpektor, and Orgad Keller.

Source: Google AI Blog


Exploring Massively Multilingual, Massive Neural Machine Translation



“... perhaps the way [of translation] is to descend, from each language, down to the common base of human communication — the real but as yet undiscovered universal language — and then re-emerge by whatever particular route is convenient.”Warren Weaver, 1949

Over the last few years there has been enormous progress in the quality of machine translation (MT) systems, breaking language barriers around the world thanks to the developments in neural machine translation (NMT). The success of NMT however, owes largely to the great amounts of supervised training data. But what about languages where data is scarce, or even absent? Multilingual NMT, with the inductive bias that “the learning signal from one language should benefit the quality of translation to other languages”, is a potential remedy.

Multilingual machine translation processes multiple languages using a single translation model. The success of multilingual training for data-scarce languages has been demonstrated for automatic speech recognition and text-to-speech systems, and by prior research on multilingual translation [1,2,3]. We previously studied the effect of scaling up the number of languages that can be learned in a single neural network, while controlling the amount of training data per language. But what happens once all constraints are removed? Can we train a single model using all of the available data, despite the huge differences across languages in data size, scripts, complexity and domains?

In “Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges” and follow-up papers [4,5,6,7], we push the limits of research on multilingual NMT by training a single NMT model on 25+ billion sentence pairs, from 100+ languages to and from English, with 50+ billion parameters. The result is an approach for massively multilingual, massive neural machine translation (M4) that demonstrates large quality improvements on both low- and high-resource languages and can be easily adapted to individual domains/languages, while showing great efficacy on cross-lingual downstream transfer tasks.

Massively Multilingual Machine Translation
Though data skew across language-pairs is a great challenge in NMT, it also creates an ideal scenario in which to study transfer, where insights gained through training on one language can be applied to the translation of other languages. On one end of the distribution, there are high-resource languages like French, German and Spanish where there are billions of parallel examples, while on the other end, supervised data for low-resource languages such as Yoruba, Sindhi and Hawaiian, is limited to a few tens of thousands.
The data distribution over all language pairs (in log scale) and the relative translation quality (BLEU score) of the bilingual baselines trained on each one of these specific language pairs.
Once trained using all of the available data (25+ billion examples from 103 languages), we observe strong positive transfer towards low-resource languages, dramatically improving the translation quality of 30+ languages at the tail of the distribution by an average of 5 BLEU points. This effect is already known, but surprisingly encouraging, considering the comparison is between bilingual baselines (i.e., models trained only on specific language pairs) and a single multilingual model with representational capacity similar to a single bilingual model. This finding hints that massively multilingual models are effective at generalization, and capable of capturing the representational similarity across a large body of languages.
Translation quality comparison of a single massively multilingual model against bilingual baselines that are trained for each one of the 103 language pairs.
In our EMNLP’19 paper [5], we compare the representations of multilingual models across different languages. We find that multilingual models learn shared representations for linguistically similar languages without the need for external constraints, validating long-standing intuitions and empirical results that exploit these similarities. In [6], we further demonstrate the effectiveness of these learned representations on cross-lingual transfer on downstream tasks.
Visualization of the clustering of the encoded representations of all 103 languages, based on representational similarity. Languages are color-coded by their linguistic family.
Building Massive Neural Networks
As we increase the number of low-resource languages in the model, the quality of high-resource language translations starts to decline. This regression is recognized in multi-task setups, arising from inter-task competition and the unidirectional nature of transfer (i.e., from high- to low-resource). While working on better learning and capacity control algorithms to mitigate this negative transfer, we also extend the representational capacity of our neural networks by making them bigger by increasing the number of model parameters to improve the quality of translation for high-resource languages.

Numerous design choices can be made to scale neural network capacity, including adding more layers or making the hidden representations wider. Continuing our study on training deeper networks for translation, we utilized GPipe [4] to train 128-layer Transformers with over 6 billion parameters. Increasing the model capacity resulted in significantly improved performance across all languages by an average of 5 BLEU points. We also studied other properties of very deep networks, including the depth-width trade-off, trainability challenges and design choices for scaling Transformers to over 1500 layers with 84 billion parameters.

While scaling depth is one approach to increasing model capacity, exploring architectures that can exploit the multi-task nature of the problem is a very plausible complementary way forward. By modifying the Transformer architecture through the substitution of the vanilla feed-forward layers with sparsely-gated mixture of experts, we drastically scale up the model capacity, allowing us to successfully train and pass 50 billion parameters, which further improved translation quality across the board.
Translation quality improvement of a single massively multilingual model as we increase the capacity (number of parameters) compared to 103 individual bilingual baselines.
Making M4 Practical
It is inefficient to train large models with extremely high computational costs for every individual language, domain or transfer task. Instead, we present methods [7] to make these models more practical by using capacity tunable layers to adapt a new model to specific languages or domains, without altering the original.

Next Steps
At least half of the 7,000 languages currently spoken will no longer exist by the end of this century*. Can multilingual machine translation come to the rescue? We see the M4 approach as a stepping stone towards serving the next 1,000 languages; starting from such multilingual models will allow us to easily extend to new languages, domains and down-stream tasks, even when parallel data is unavailable. Indeed the path is rocky, and on the road to universal MT many promising solutions appear to be interdisciplinary. This makes multilingual NMT a plausible test bed for machine learning practitioners and theoreticians interested in exploring the annals of multi-task learning, meta-learning, training dynamics of deep nets and much more. We still have a long way to go.

Acknowledgements
This effort is built on contributions from Naveen Arivazhagan, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Chen, Yuan Cao, Yanping Huang, Sneha Kudugunta, Isaac Caswell, Aditya Siddhant, Wei Wang, Roee Aharoni, Sébastien Jean, George Foster, Colin Cherry, Wolfgang Macherey, Zhifeng Chen and Yonghui Wu. We would also like to acknowledge support from the Google Translate, Brain, and Lingvo development teams, Jakob Uszkoreit, Noam Shazeer, Hyouk Joong Lee, Dehao Chen, Youlong Cheng, David Grangier, Colin Raffel, Katherine Lee, Thang Luong, Geoffrey Hinton, Manisha Jain, Pendar Yousefi and Macduff Hughes.


* The Cambridge Handbook of Endangered Languages (Austin and Sallabank, 2011).

Source: Google AI Blog