Identifying attack and support argumentative relations using deep learning

Oana Cocarascu, Francesca Toni


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.
Anthology ID:
D17-1144
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:
1374–1379
Language:
URL:
https://www.aclweb.org/anthology/D17-1144
DOI:
10.18653/v1/D17-1144
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1144.pdf