Structured Representation Learning for Online Debate Stance Prediction

Chang Li, Aldo Porco, Dan Goldwasser


Abstract
Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.
Anthology ID:
C18-1316
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3728–3739
Language:
URL:
https://www.aclweb.org/anthology/C18-1316
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-1316.pdf