Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates

Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu


Abstract
Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.
Anthology ID:
D18-1069
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
665–670
Language:
URL:
https://www.aclweb.org/anthology/D18-1069
DOI:
10.18653/v1/D18-1069
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1069.pdf
Video:
 https://vimeo.com/306360792