ReadTwice: Reading Very Large Documents with Memories

• URL: http://arxiv.org/abs/2105.04241v2
• Date: Tue, 11 May 2021 23:07:13 GMT
• Title: ReadTwice: Reading Very Large Documents with Memories
• Authors: Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, Fei Sha
• Abstract summary: We propose ReadTwice, a 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.
• Score: 19.45538971299312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Source code and pre-trained checkpoints for ReadTwice can be found at https://goo.gle/research-readtwice.

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