Abstract
This paper describes the system and results of our team participated in SemEval-2020 Task4: Commonsense Validation and Explanation (ComVE), which aim to distinguish meaningful natural language statements from unreasonable natural language statements. This task contains three subtasks: Subtask A–Validation, Subtask B–Explanation (Multi-Choice), and Subtask C– Explanation (Generation). In these three subtasks, we only participated in Subtask A, which aims to distinguish whether a given two natural language statements with similar wording are meaningful. To solve this problem, we proposed a method using a combination of BERT with the Bidirectional Gated Recurrent Unit (Bi-GRU). Our model achieved an accuracy of 0.836 in Subtask A (ranked 27/45).- Anthology ID:
- 2020.semeval-1.80
- 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:
- 626–632
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.80
- DOI:
- 10.18653/v1/2020.semeval-1.80
- Cite (ACL):
- Xiaozhi Ou and Hongling Li. 2020. YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 626–632, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU (Ou & Li, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.semeval-1.80.pdf
- Data
- WSC