ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task

Junfeng Tian, Man Lan, Yuanbin Wu


Abstract
This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4% accuracy and ranks 3rd among 22 participating systems.
Anthology ID:
S18-1184
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:
1094–1098
Language:
URL:
https://www.aclweb.org/anthology/S18-1184
DOI:
10.18653/v1/S18-1184
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1184.pdf