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        <title>Here’s My Point: Joint Pointer Architecture for Argument Mining</title>
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    <abstract>In order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link extraction in argument mining. Specifically, we propose a novel architecture that applies Pointer Network sequence-to-sequence attention modeling to structural prediction in discourse parsing tasks. We then develop a joint model that extends this architecture to simultaneously address the link extraction task and the classification of argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, showing far superior performance than the previously proposed corpus-specific and heavily feature-engineered models. Furthermore, our results demonstrate that jointly optimizing for both tasks is crucial for high performance.</abstract>
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%0 Conference Proceedings
%T Here’s My Point: Joint Pointer Architecture for Argument Mining
%A Potash, Peter
%A Romanov, Alexey
%A Rumshisky, Anna
%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 potash-etal-2017-heres
%X In order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link extraction in argument mining. Specifically, we propose a novel architecture that applies Pointer Network sequence-to-sequence attention modeling to structural prediction in discourse parsing tasks. We then develop a joint model that extends this architecture to simultaneously address the link extraction task and the classification of argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, showing far superior performance than the previously proposed corpus-specific and heavily feature-engineered models. Furthermore, our results demonstrate that jointly optimizing for both tasks is crucial for high performance.
%R 10.18653/v1/D17-1143
%U https://aclanthology.org/D17-1143
%U https://doi.org/10.18653/v1/D17-1143
%P 1364-1373

Markdown (Informal)

[Here’s My Point: Joint Pointer Architecture for Argument Mining](https://aclanthology.org/D17-1143) (Potash et al., EMNLP 2017)

• Here’s My Point: Joint Pointer Architecture for Argument Mining (Potash et al., EMNLP 2017)

ACL

• Peter Potash, Alexey Romanov, and Anna Rumshisky. 2017. Here’s My Point: Joint Pointer Architecture for Argument Mining. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1364–1373, Copenhagen, Denmark. Association for Computational Linguistics.