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    <abstract>The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.</abstract>
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%0 Conference Proceedings
%T A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension
%A Liu, Jiahua
%A Wei, Wan
%A Sun, Maosong
%A Chen, Hao
%A Du, Yantao
%A Lin, Dekang
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-etal-2018-multi
%X The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.
%R 10.18653/v1/D18-1235
%U https://aclanthology.org/D18-1235
%U https://doi.org/10.18653/v1/D18-1235
%P 2109-2118

Markdown (Informal)

[A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension](https://aclanthology.org/D18-1235) (Liu et al., EMNLP 2018)

• A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (Liu et al., EMNLP 2018)

ACL

• Jiahua Liu, Wan Wei, Maosong Sun, Hao Chen, Yantao Du, and Dekang Lin. 2018. A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2109–2118, Brussels, Belgium. Association for Computational Linguistics.