Tag Archives: Information Retrieval

TF-Ranking: A Scalable TensorFlow Library for Learning-to-Rank

Posted by Xuanhui Wang and Michael Bendersky, Software Engineers, Google AI

Ranking, the process of ordering a list of items in a way that maximizes the utility of the entire list, is applicable in a wide range of domains, from search engines and recommender systems to machine translation, dialogue systems and even computational biology. In applications like these (and many others), researchers often utilize a set of supervised machine learning techniques called learning-to-rank. In many cases, these learning-to-rank techniques are applied to datasets that are prohibitively large — scenarios where the scalability of TensorFlow could be an advantage. However, there is currently no out-of-the-box support for applying learning-to-rank techniques in TensorFlow. To the best of our knowledge, there are also no other open source libraries that specialize in applying learning-to-rank techniques at scale.

Today, we are excited to share TF-Ranking, a scalable TensorFlow-based library for learning-to-rank. As described in our recent paper, TF-Ranking provides a unified framework that includes a suite of state-of-the-art learning-to-rank algorithms, and supports pairwise or listwise loss functions, multi-item scoring, ranking metric optimization, and unbiased learning-to-rank.

TF-Ranking is fast and easy to use, and creates high-quality ranking models. The unified framework gives ML researchers, practitioners and enthusiasts the ability to evaluate and choose among an array of different ranking models within a single library. Moreover, we strongly believe that a key to a useful open source library is not only providing sensible defaults, but also empowering our users to develop their own custom models. Therefore, we provide flexible API's, within which the users can define and plug in their own customized loss functions, scoring functions and metrics.

Existing Algorithms and Metrics Support
The objective of learning-to-rank algorithms is minimizing a loss function defined over a list of items to optimize the utility of the list ordering for any given application. TF-Ranking supports a wide range of standard pointwise, pairwise and listwise loss functions as described in prior work. This ensures that researchers using the TF-Ranking library are able to reproduce and extend previously published baselines, and practitioners can make the most informed choices for their applications. Furthermore, TF-Ranking can handle sparse features (like raw text) through embeddings and scales to hundreds of millions of training instances. Thus, anyone who is interested in building real-world data intensive ranking systems such as web search or news recommendation, can use TF-Ranking as a robust, scalable solution.

Empirical evaluation is an important part of any machine learning or information retrieval research. To ensure compatibility with prior work, we support many of the commonly used ranking metrics, including Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). We also make it easy to visualize these metrics at training time on TensorBoard, an open source TensorFlow visualization dashboard.
An example of the NDCG metric (Y-axis) along the training steps (X-axis) displayed in the TensorBoard. It shows the overall progress of the metrics during training. Different methods can be compared directly on the dashboard. Best models can be selected based on the metric.
Multi-Item Scoring
TF-Ranking supports a novel scoring mechanism wherein multiple items (e.g., web pages) can be scored jointly, an extension of the traditional scoring paradigm in which single items are scored independently. One challenge in multi-item scoring is the difficulty for inference where items have to be grouped and scored in subgroups. Then, scores are accumulated per-item and used for sorting. To make these complexities transparent to the user, TF-Ranking provides a List-In-List-Out (LILO) API to wrap all this logic in the exported TF models.
The TF-Ranking library supports multi-item scoring architecture, an extension of traditional single-item scoring.
As we demonstrate in recent work, multi-item scoring is competitive in its performance to the state-of-the-art learning-to-rank models such as RankNet, MART, and LambdaMART on a public LETOR benchmark.

Ranking Metric Optimization
An important research challenge in learning-to-rank is direct optimization of ranking metrics (such as the previously mentioned NDCG and MRR). These metrics, while being able to measure the performance of ranking systems better than the standard classification metrics like Area Under the Curve (AUC), have the unfortunate property of being either discontinuous or flat. Therefore standard stochastic gradient descent optimization of these metrics is problematic.

In recent work, we proposed a novel method, LambdaLoss, which provides a principled probabilistic framework for ranking metric optimization. In this framework, metric-driven loss functions can be designed and optimized by an expectation-maximization procedure. The TF-Ranking library integrates the recent advances in direct metric optimization and provides an implementation of LambdaLoss. We are hopeful that this will encourage and facilitate further research advances in the important area of ranking metric optimization.

Unbiased Learning-to-Rank
Prior research has shown that given a ranked list of items, users are much more likely to interact with the first few results, regardless of their relevance. This observation has inspired research interest in unbiased learning-to-rank, and led to the development of unbiased evaluation and several unbiased learning algorithms, based on training instances re-weighting. In the TF-Ranking library, metrics are implemented to support unbiased evaluation and losses are implemented for unbiased learning by natively supporting re-weighting to overcome the inherent biases in user interactions datasets.

Getting Started with TF-Ranking
TF-Ranking implements the TensorFlow Estimator interface, which greatly simplifies machine learning programming by encapsulating training, evaluation, prediction and export for serving. TF-Ranking is well integrated with the rich TensorFlow ecosystem. As described above, you can use Tensorboard to visualize ranking metrics like NDCG and MRR, as well as to pick the best model checkpoints using these metrics. Once your model is ready, it is easy to deploy it in production using TensorFlow Serving.

If you’re interested in trying TF-Ranking for yourself, please check out our GitHub repo, and walk through the tutorial examples. TF-Ranking is an active research project, and we welcome your feedback and contributions. We are excited to see how TF-Ranking can help the information retrieval and machine learning research communities.

Acknowledgements
This project was only possible thanks to the members of the core TF-Ranking team: Rama Pasumarthi, Cheng Li, Sebastian Bruch, Nadav Golbandi, Stephan Wolf, Jan Pfeifer, Rohan Anil, Marc Najork, Patrick McGregor and Clemens Mewald‎. We thank the members of the TensorFlow team for their advice and support: Alexandre Passos, Mustafa Ispir, Karmel Allison, Martin Wicke, and others. Finally, we extend our special thanks to our collaborators, interns and early adopters: Suming Chen, Zhen Qin, Chirag Sethi, Maryam Karimzadehgan, Makoto Uchida, Yan Zhu, Qingyao Ai, Brandon Tran, Donald Metzler, Mike Colagrosso, and many others at Google who helped in evaluating and testing the early versions of TF-Ranking.

Source: Google AI Blog


Evaluation of Speech for the Google Assistant

Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant

Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music. The Google Assistant is designed to provide help and information across a variety of platforms, and is built to bring together a number of products — including Google Maps, Search, Google Photos, third party services, and more. For some of these products, we have released specific evaluation guidelines, like Search Quality Rating Guidelines. However, the Google Assistant needs its own guidelines in place, as many of its interactions utilize what is called “eyes-free technology,” when there is no screen as part of the experience.

In the past we have received requests to see our evaluation guidelines from academics who are researching improvements in voice interactions, question answering and voice-guided exploration. To facilitate their evaluations, we are publishing some of the first Google Assistant guidelines. It is our hope that making these guidelines public will help the research community build and evaluate their own systems.

Creating the Guidelines
For many queries, responses are presented on the display (like a phone) with a graph, a table, or an interactive element, like you’d see for [weather this weekend].
But spoken responses are very different from display results, as what’s on screen needs to be translated into useful speech. Furthermore, the contents of the voice response are sometimes sourced from the web, and in those cases it’s important to provide the user with a link to the original source. While users looking at their mobile device can click through to read the original web page, an eyes free solution presents unique challenges. In order to generate the optimal audio response, we use a combination of explicit linguistic knowledge and deep learning solutions that allow us to keep answers grammatical, fluent and concise.

How do we ensure that we consistently meet user expectations on quality, across all answer types and languages? One of the tools we use to measure that are human evaluations. In these, we ask raters to make sure that answers are satisfactory across several dimensions:
  • Information Satisfaction: the content of the answer should meet the information needs of the user.
  • Length: when a displayed answer is too long, users can quickly scan it visually and locate the relevant information. For voice answers, that is not possible. It is much more important to ensure that we provide a helpful amount of information, hopefully not too much or too little. Some of our previous work is currently in use for identifying the most relevant fragments of answers.
  • Formulation: it is much easier to understand a badly formulated written answer than an ungrammatical spoken answer, so more care has to be placed in ensuring grammatical correctness.
  • Elocution: spoken answers must have proper pronunciation and prosody. Improvements in text-to-speech generation, such as WaveNet and Tacotron 2, are quickly reducing the gap with human performance.
The current version of the guidelines can be found here. Of course, guidelines are often updated, and these are just a snapshot of something that is a living, changing, always-work-in-progress evaluation!