Abstract
We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.- Anthology ID:
- 2021.rocling-1.50
- Volume:
- Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
- Month:
- October
- Year:
- 2021
- Address:
- Taoyuan, Taiwan
- Editors:
- Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
- Venue:
- ROCLING
- SIG:
- Publisher:
- The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
- Note:
- Pages:
- 380–384
- Language:
- URL:
- https://aclanthology.org/2021.rocling-1.50
- DOI:
- Cite (ACL):
- Man-Chen Hung, Chao-Yi Chen, Pin-Jung Chen, and Lung-Hao Lee. 2021. NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 380–384, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
- Cite (Informal):
- NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis (Hung et al., ROCLING 2021)
- PDF:
- https://preview.aclanthology.org/autopr/2021.rocling-1.50.pdf