Abstract
This paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 Task 4. Our systems rely on pre-trained language models, i.e., BERT (including its variants) and UniLM, and rank 10th and 7th among 27 and 17 systems on Subtasks B and C, respectively. We analyze the commonsense ability of the existing pretrained language models by testing them on the SemEval-2020 Task 4 ComVE dataset, specifically for Subtasks B and C, the explanation subtasks with multi-choice and sentence generation, respectively.- Anthology ID:
- 2020.semeval-1.65
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 527–534
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.65
- DOI:
- 10.18653/v1/2020.semeval-1.65
- Cite (ACL):
- Seung-Hoon Na and Jong-Hyeon Lee. 2020. JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 527–534, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation (Na & Lee, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.65.pdf