TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension

Zhimin Chen, Wei Song, Lizhen Liu


Abstract
This paper describes our system in SemEval-2018 task 12: Argument Reasoning Comprehension. The task is to select the correct warrant that explains reasoning of a particular argument consisting of a claim and a reason. The main idea of our methods is based on the assumption that the semantic composition of the reason and the warrant should be close to the semantic representation of the corresponding claim. We propose two neural network models. The first one considers two warrant candidates simultaneously, while the second one processes each candidate separately and then chooses the best one. We also incorporate sentiment polarity by assuming that there are kinds of sentiment associations between the reason, the warrant and the claim. The experiments show that the first framework is more effective and sentiment polarity is useful.
Anthology ID:
S18-1194
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1142–1145
Language:
URL:
https://www.aclweb.org/anthology/S18-1194
DOI:
10.18653/v1/S18-1194
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1194.pdf