Abstract
This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options. We decided to use a deep learning architecture and combined 623 models with different hyperparameters into an ensemble. Our extensive analysis of our architecture and ensemble reveals that the decision to use an ensemble was suboptimal. Additionally, we benchmark a support vector machine as a baseline. Furthermore, we experimented with an alternative data split and achieved more stable results.- Anthology ID:
- S18-1188
- 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:
- 1114–1119
- Language:
- URL:
- https://aclanthology.org/S18-1188
- DOI:
- 10.18653/v1/S18-1188
- Cite (ACL):
- Matthias Liebeck, Andreas Funke, and Stefan Conrad. 2018. HHU at SemEval-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct Warrant. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1114–1119, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- HHU at SemEval-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct Warrant (Liebeck et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S18-1188.pdf