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.

ReadTwice: Reading Very Large Documents with Memories



<i>**Session Chair: ** Taylor Berg-Kirkpatrick </i><br><br>

14D-Oral: Machine Learning for NLP: Language Modeling and Sequence to Sequence Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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ReadTwice: Reading Very Large Documents with Memories

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