YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model

Quanlei Liao, Xutao Yang, Jin Wang, Xuejie Zhang


Abstract
An argument is divided into two parts, the claim and the reason. To obtain a clearer conclusion, some additional explanation is required. In this task, the explanations are called warrants. This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument. We address this question as a question-answering system. For each warrant, the model produces a probability that it is correct. Finally, the system chooses the highest correct probability as the answer. Ensemble learning is used to enhance the performance of the model. Among all of the participants, we ranked 15th on the test results.
Anthology ID:
S18-1187
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:
1109–1113
Language:
URL:
https://www.aclweb.org/anthology/S18-1187
DOI:
10.18653/v1/S18-1187
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1187.pdf