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        <title>Neural End-to-End Learning for Computational Argumentation Mining</title>
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        <namePart type="given">Steffen</namePart>
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        <namePart type="given">Johannes</namePart>
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    <name type="personal">
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1002</identifier>
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%0 Conference Proceedings
%T Neural End-to-End Learning for Computational Argumentation Mining
%A Eger, Steffen
%A Daxenberger, Johannes
%A Gurevych, Iryna
%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 eger-etal-2017-neural
%X We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.
%R 10.18653/v1/P17-1002
%U https://aclanthology.org/P17-1002
%U https://doi.org/10.18653/v1/P17-1002
%P 11-22

Markdown (Informal)

[Neural End-to-End Learning for Computational Argumentation Mining](https://aclanthology.org/P17-1002) (Eger et al., ACL 2017)

• Neural End-to-End Learning for Computational Argumentation Mining (Eger et al., ACL 2017)

ACL

• Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. 2017. Neural End-to-End Learning for Computational Argumentation Mining. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11–22, Vancouver, Canada. Association for Computational Linguistics.