BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention

Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu


Abstract
To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind. In this paper, we propose a hierarchical attention model to identify the right warrant which explains why the reason stands for the claim. Our model focuses not only on the similar part between warrants and other information but also on the contradictory part between two opposing warrants. In addition, we use the ensemble method for different models. Our model achieves an accuracy of 61%, ranking second in this task. Experimental results demonstrate that our model is effective to make correct choices.
Anthology ID:
S18-1186
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
SemEval | *SEM
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1104–1108
Language:
URL:
https://aclanthology.org/S18-1186
DOI:
10.18653/v1/S18-1186
Bibkey:
Cite (ACL):
Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, and Dong Yu. 2018. BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1104–1108, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention (Zhao et al., SemEval-*SEM 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/S18-1186.pdf