@inproceedings{zemlyanskiy-etal-2021-readtwice,
title = "{R}ead{T}wice: Reading Very Large Documents with Memories",
author = "Zemlyanskiy, Yury and
Ainslie, Joshua and
de Jong, Michiel and
Pham, Philip and
Eckstein, Ilya and
Sha, Fei",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.408",
doi = "10.18653/v1/2021.naacl-main.408",
pages = "5189--5195",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T ReadTwice: Reading Very Large Documents with Memories
%A Zemlyanskiy, Yury
%A Ainslie, Joshua
%A de Jong, Michiel
%A Pham, Philip
%A Eckstein, Ilya
%A Sha, Fei
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F zemlyanskiy-etal-2021-readtwice
%X 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.
%R 10.18653/v1/2021.naacl-main.408
%U https://aclanthology.org/2021.naacl-main.408
%U https://doi.org/10.18653/v1/2021.naacl-main.408
%P 5189-5195
Markdown (Informal)
[ReadTwice: Reading Very Large Documents with Memories](https://aclanthology.org/2021.naacl-main.408) (Zemlyanskiy et al., NAACL 2021)
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.