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

Peter Potash, Alexey Romanov, Anna Rumshisky


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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1364–1373
Language:
URL:
https://www.aclweb.org/anthology/D17-1143
DOI:
10.18653/v1/D17-1143
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1143.pdf