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.- Anthology ID:
- S18-1194
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1142–1145
- Language:
- URL:
- https://aclanthology.org/S18-1194
- DOI:
- 10.18653/v1/S18-1194
- Cite (ACL):
- Zhimin Chen, Wei Song, and Lizhen Liu. 2018. TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1142–1145, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension (Chen et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S18-1194.pdf