Illia Polosukhin


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Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski | Jennimaria Palomaki | Olivia Redfield | Michael Collins | Ankur Parikh | Chris Alberti | Danielle Epstein | Illia Polosukhin | Jacob Devlin | Kenton Lee | Kristina Toutanova | Llion Jones | Matthew Kelcey | Ming-Wei Chang | Andrew M. Dai | Jakob Uszkoreit | Quoc Le | Slav Petrov
Transactions of the Association for Computational Linguistics, Volume 7

We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.


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Coarse-to-Fine Question Answering for Long Documents
Eunsol Choi | Daniel Hewlett | Jakob Uszkoreit | Illia Polosukhin | Alexandre Lacoste | Jonathan Berant
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate state-of-the-art performance on a challenging subset of the WikiReading dataset and on a new dataset, while speeding up the model by 3.5x-6.7x.


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WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
Daniel Hewlett | Alexandre Lacoste | Llion Jones | Illia Polosukhin | Andrew Fandrianto | Jay Han | Matthew Kelcey | David Berthelot
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)