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        <title>Search-based Neural Structured Learning for Sequential Question Answering</title>
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        <namePart type="given">Mohit</namePart>
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        <namePart type="given">Wen-tau</namePart>
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    <name type="personal">
        <namePart type="given">Ming-Wei</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1167</identifier>
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%0 Conference Proceedings
%T Search-based Neural Structured Learning for Sequential Question Answering
%A Iyyer, Mohit
%A Yih, Wen-tau
%A Chang, Ming-Wei
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F iyyer-etal-2017-search
%X Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.
%R 10.18653/v1/P17-1167
%U https://aclanthology.org/P17-1167
%U https://doi.org/10.18653/v1/P17-1167
%P 1821-1831

Markdown (Informal)

[Search-based Neural Structured Learning for Sequential Question Answering](https://aclanthology.org/P17-1167) (Iyyer et al., ACL 2017)

• Search-based Neural Structured Learning for Sequential Question Answering (Iyyer et al., ACL 2017)

ACL

• Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017. Search-based Neural Structured Learning for Sequential Question Answering. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1821–1831, Vancouver, Canada. Association for Computational Linguistics.