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    <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.</abstract>
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%0 Conference Proceedings
%T A Simple and Effective Model for Answering Multi-span Questions
%A Segal, Elad
%A Efrat, Avia
%A Shoham, Mor
%A Globerson, Amir
%A Berant, Jonathan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F segal-etal-2020-simple
%X 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.
%R 10.18653/v1/2020.emnlp-main.248
%U https://aclanthology.org/2020.emnlp-main.248
%U https://doi.org/10.18653/v1/2020.emnlp-main.248
%P 3074-3080

Markdown (Informal)

[A Simple and Effective Model for Answering Multi-span Questions](https://aclanthology.org/2020.emnlp-main.248) (Segal et al., EMNLP 2020)

• A Simple and Effective Model for Answering Multi-span Questions (Segal et al., EMNLP 2020)

ACL

• Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, and Jonathan Berant. 2020. A Simple and Effective Model for Answering Multi-span Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3074–3080, Online. Association for Computational Linguistics.