Abstract
This paper describes BUT-FIT’s submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text (Subtask 2). We experimented with various state-of-the-art language representation models (LRMs). We found RoBERTa LRM to perform the best in both subtasks. We achieved the first place in both exact match and F1 for Subtask 2 and ranked second for Subtask 1.- Anthology ID:
- 2020.semeval-1.53
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 437–444
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.53
- DOI:
- 10.18653/v1/2020.semeval-1.53
- Cite (ACL):
- Martin Fajcik, Josef Jon, Martin Docekal, and Pavel Smrz. 2020. BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 437–444, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models (Fajcik et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.semeval-1.53.pdf
- Code
- MFajcik/SemEval_2020_Task-5