Here’s My Point: Joint Pointer Architecture for Argument Mining

Peter Potash, Alexey Romanov, Anna Rumshisky

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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.
Anthology ID:
D17-1143
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1364–1373
Language:
URL:
https://aclanthology.org/D17-1143
DOI:
10.18653/v1/D17-1143
Bibkey:
Cite (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.
Cite (Informal):
Here’s My Point: Joint Pointer Architecture for Argument Mining (Potash et al., EMNLP 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D17-1143.pdf
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