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        <title>Neural Argument Generation Augmented with Externally Retrieved Evidence</title>
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        <namePart type="given">Xinyu</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1021</identifier>
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%0 Conference Proceedings
%T Neural Argument Generation Augmented with Externally Retrieved Evidence
%A Hua, Xinyu
%A Wang, Lu
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F hua-wang-2018-neural
%X High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.
%R 10.18653/v1/P18-1021
%U https://aclanthology.org/P18-1021
%U https://doi.org/10.18653/v1/P18-1021
%P 219-230

Markdown (Informal)

[Neural Argument Generation Augmented with Externally Retrieved Evidence](https://aclanthology.org/P18-1021) (Hua & Wang, ACL 2018)

• Neural Argument Generation Augmented with Externally Retrieved Evidence (Hua & Wang, ACL 2018)

ACL

• Xinyu Hua and Lu Wang. 2018. Neural Argument Generation Augmented with Externally Retrieved Evidence. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 219–230, Melbourne, Australia. Association for Computational Linguistics.