Abstract
In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information. Our model achieved three of the best results in four metrics on “ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts”. We conducted a series of experiments to compare the effectiveness of different pre-trained methods. Besides, the results also proofed that our method can significantly improve the performances than classic methods. Based on the experiments, we also discussed the impact of model architectures and datasets.- Anthology ID:
- 2021.rocling-1.47
- 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:
- 360–366
- Language:
- URL:
- https://aclanthology.org/2021.rocling-1.47
- DOI:
- Cite (ACL):
- Ke-Han Lu and Kuan-Yu Chen. 2021. ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 360–366, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
- Cite (Informal):
- ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information (Lu & Chen, ROCLING 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.rocling-1.47.pdf