EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction
Xue Gu, Zhihan Zhou, Ziyao Meng, Jian Li, Tiago Gomes, Adriano Tavares, Hao Xu
Abstract
Emotion-cause pair extraction (ECPE) main focus is on extracting all potential emotion clauses and corresponding cause clauses from unannotated documents. Existing methods achieve promising results with the help of fine-tuning and prompt paradigms, but they present three downsides. First, most approaches cannot distinguish between the emotion-cause pairs that belong to different types of emotions, limiting the existing approaches’ applicability. Second, existing prompt methods utilize a one-to-one mapping relation to achieve label words to category mapping, which brings considerable bias to the results. Third, existing methods achieve the cause extraction task supported by explicit semantic understanding or basic prompt templates, ignoring the implicit information contained in the cause clauses themselves. To solve these issues, we propose an Emotion knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (EmoPrompt-ECPE) method, which integrate the knowledge of emotion categories in the ECPE task and mine the implicit knowledge of cause clauses. Specifically, we inject the latent knowledge of the cause clauses and the emotion types into the prompt template. Besides, we extend the emotion labels for many-to-one mapping of label words to categories with an external emotion word base. Furthermore, we utilize the cosine similarity filtering of the label word base to reduce the noise caused by knowledge introduction. Experiments on both Chinese and English benchmark datasets show that our approach can achieve state-of-the-art results. Our code and data can be found at: https://github.com/xy-xiaotudou/EmoPrompt-ECPE.- Anthology ID:
- 2024.lrec-main.504
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 5678–5688
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.504
- DOI:
- Cite (ACL):
- Xue Gu, Zhihan Zhou, Ziyao Meng, Jian Li, Tiago Gomes, Adriano Tavares, and Hao Xu. 2024. EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5678–5688, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (Gu et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.lrec-main.504.pdf