ReadTwice: Reading Very Large Documents with Memories

Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, Fei Sha


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.
Anthology ID:
2021.naacl-main.408
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5189–5195
Language:
URL:
https://aclanthology.org/2021.naacl-main.408
DOI:
10.18653/v1/2021.naacl-main.408
Bibkey:
Cite (ACL):
Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, and Fei Sha. 2021. ReadTwice: Reading Very Large Documents with Memories. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5189–5195, Online. Association for Computational Linguistics.
Cite (Informal):
ReadTwice: Reading Very Large Documents with Memories (Zemlyanskiy et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.408.pdf
Video:
 https://aclanthology.org/2021.naacl-main.408.mp4
Data
HotpotQANarrativeQATriviaQA