@inproceedings{chen-etal-2018-transrw,
title = "{TRANSRW} at {S}em{E}val-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension",
author = "Chen, Zhimin and
Song, Wei and
Liu, Lizhen",
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/S18-1194",
doi = "10.18653/v1/S18-1194",
pages = "1142--1145",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension
%A Chen, Zhimin
%A Song, Wei
%A Liu, Lizhen
%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 chen-etal-2018-transrw
%X 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.
%R 10.18653/v1/S18-1194
%U https://aclanthology.org/S18-1194
%U https://doi.org/10.18653/v1/S18-1194
%P 1142-1145
Markdown (Informal)
[TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension](https://aclanthology.org/S18-1194) (Chen et al., SemEval 2018)
ACL