Ilya Eckstein


2021

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ReadTwice: Reading Very Large Documents with Memories
Yury Zemlyanskiy | Joshua Ainslie | Michiel de Jong | Philip Pham | Ilya Eckstein | Fei Sha
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Knowledge-intensive tasks such as question answering often require assimilating information from different sections of large inputs such as books or article collections. We propose ReadTwice, a simple and effective technique that combines several strengths of prior approaches to model long-range dependencies with Transformers. The main idea is to read text in small segments, in parallel, summarizing each segment into a memory table to be used in a second read of the text. We show that the method outperforms models of comparable size on several question answering (QA) datasets and sets a new state of the art on the challenging NarrativeQA task, with questions about entire books.

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DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections
Yury Zemlyanskiy | Sudeep Gandhe | Ruining He | Bhargav Kanagal | Anirudh Ravula | Juraj Gottweis | Fei Sha | Ilya Eckstein
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

This paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion, question answering, and more. Unlike other methods that harvest self-supervision signals based merely on a local context within a sentence, we radically expand the notion of context to include any available text related to an entity. This enables a new class of powerful, high-capacity representations that can ultimately distill much of the useful information about an entity from multiple text sources, without any human supervision. We present several training strategies that, unlike prior approaches, learn to jointly predict words and entities – strategies we compare experimentally on downstream tasks in the TV-Movies domain, such as MovieLens tag prediction from user reviews and natural language movie search. As evidenced by results, our models match or outperform competitive baselines, sometimes with little or no fine-tuning, and are also able to scale to very large corpora. Finally, we make our datasets and pre-trained models publicly available. This includes Reviews2Movielens, mapping the ~1B word corpus of Amazon movie reviews (He and McAuley, 2016) to MovieLens tags (Harper and Konstan, 2016), as well as Reddit Movie Suggestions with natural language queries and corresponding community recommendations.