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        <title>Question Answering by Reasoning Across Documents with Graph Convolutional Networks</title>
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        <namePart type="given">Nicola</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
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            <namePart type="given">Jill</namePart>
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            <namePart type="given">Christy</namePart>
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    <abstract>Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).</abstract>
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%0 Conference Proceedings
%T Question Answering by Reasoning Across Documents with Graph Convolutional Networks
%A De Cao, Nicola
%A Aziz, Wilker
%A Titov, Ivan
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F de-cao-etal-2019-question
%X Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).
%R 10.18653/v1/N19-1240
%U https://aclanthology.org/N19-1240
%U https://doi.org/10.18653/v1/N19-1240
%P 2306-2317

Markdown (Informal)

[Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://aclanthology.org/N19-1240) (De Cao et al., NAACL 2019)

• Question Answering by Reasoning Across Documents with Graph Convolutional Networks (De Cao et al., NAACL 2019)

ACL

• Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019. Question Answering by Reasoning Across Documents with Graph Convolutional Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2306–2317, Minneapolis, Minnesota. Association for Computational Linguistics.