Abstract
Introducing common sense to natural language understanding systems has received increasing research attention. To facilitate the researches on common sense reasoning, the SemEval-2020 Task 4 Commonsense Validation and Explanation(ComVE) is proposed. We participate in sub-task A and try various methods including traditional machine learning methods, deep learning methods, and also recent pre-trained language models. Finally, we concatenate the original output of BERT and the output vector of BERT hidden layer state to obtain more abundant semantic information features, and obtain competitive results. Our model achieves an accuracy of 0.8510 in the final test data and ranks 25th among all the teams.- Anthology ID:
- 2020.semeval-1.63
- 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:
- 516–520
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.63
- DOI:
- 10.18653/v1/2020.semeval-1.63
- Cite (ACL):
- Yang Bai and Xiaobing Zhou. 2020. DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 516–520, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense (Bai & Zhou, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.63.pdf