A simple and effective model for answering multi-span questions

Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan Berant

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

51 Scopus citations

Abstract

Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be restrictive, and some recent datasets also include multi-span questions, i.e., questions whose answer is a set of non-contiguous spans in the text. Naturally, models that return single spans cannot answer these questions. In this work, we propose a simple architecture for answering multi-span questions by casting the task as a sequence tagging problem, namely, predicting for each input token whether it should be part of the output or not. Our model substantially improves performance on span extraction questions from DROP and QUOREF by 9.9 and 5.5 EM points respectively.

Original languageEnglish
Title of host publicationEMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages3074-3080
Number of pages7
ISBN (Electronic)9781952148606
StatePublished - 2020
Event2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online
Duration: 16 Nov 202020 Nov 2020

Publication series

NameEMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020
CityVirtual, Online
Period16/11/2020/11/20

Funding

FundersFunder number
European Union Horizons 2020 research and innovation programmeDELPHI 802800
Yandex Initiative for Machine Learning
Horizon 2020 Framework Programme802800
European Research Council
Israel Science Foundation1186/18, 942/16

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