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        <title>Identifying attack and support argumentative relations using deep learning</title>
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        <namePart type="given">Oana</namePart>
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        <namePart type="given">Francesca</namePart>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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        <name type="personal">
            <namePart type="given">Rebecca</namePart>
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    <abstract>We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1144</identifier>
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%0 Conference Proceedings
%T Identifying attack and support argumentative relations using deep learning
%A Cocarascu, Oana
%A Toni, Francesca
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F cocarascu-toni-2017-identifying
%X We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining.
%R 10.18653/v1/D17-1144
%U https://aclanthology.org/D17-1144
%U https://doi.org/10.18653/v1/D17-1144
%P 1374-1379

Markdown (Informal)

[Identifying attack and support argumentative relations using deep learning](https://aclanthology.org/D17-1144) (Cocarascu & Toni, EMNLP 2017)

• Identifying attack and support argumentative relations using deep learning (Cocarascu & Toni, EMNLP 2017)

ACL

• Oana Cocarascu and Francesca Toni. 2017. Identifying attack and support argumentative relations using deep learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1374–1379, Copenhagen, Denmark. Association for Computational Linguistics.