@inproceedings{zhao-etal-2018-blcu,
title = "{BLCU}{\_}{NLP} at {S}em{E}val-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention",
author = "Zhao, Meiqian and
Liu, Chunhua and
Liu, Lu and
Zhao, Yan and
Yu, Dong",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1186",
doi = "10.18653/v1/S18-1186",
pages = "1104--1108",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention
%A Zhao, Meiqian
%A Liu, Chunhua
%A Liu, Lu
%A Zhao, Yan
%A Yu, Dong
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F zhao-etal-2018-blcu
%X 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.
%R 10.18653/v1/S18-1186
%U https://aclanthology.org/S18-1186
%U https://doi.org/10.18653/v1/S18-1186
%P 1104-1108
Markdown (Informal)
[BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention](https://aclanthology.org/S18-1186) (Zhao et al., SemEval 2018)
ACL