Abstract
The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.- Anthology ID:
- 2024.semeval-1.110
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 770–776
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.110
- DOI:
- Cite (ACL):
- Hongyu Guo, Xueyao Zhang, Yiyang Chen, Lin Deng, and Binyang Li. 2024. UIR-ISC at SemEval-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 770–776, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- UIR-ISC at SemEval-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations (Guo et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.110.pdf