Abstract
ECPE (emotion cause pair extraction) task was introduced to solve the shortcomings of ECE (emotion cause extraction). Models with sequential data processing abilities or complex architecture can be utilized to solve this task. Our contribution to solving Subtask 1: Textual Emotion-Cause Pair Extraction in Conversations defined in the SemEval-2024 Task 3: The Competition of Multimodal Emotion Cause Analysis in Conversations is to create a two-step solution to the ECPE task utilizing GPT-3 for emotion classification and SpanBERT for extracting the cause utterances.- Anthology ID:
- 2024.semeval-1.159
- 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:
- 1097–1105
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.159
- DOI:
- Cite (ACL):
- Sofiia Levchenko, Rafał Wolert, and Piotr Andruszkiewicz. 2024. PWEITINLP at SemEval-2024 Task 3: Two Step Emotion Cause Analysis. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1097–1105, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PWEITINLP at SemEval-2024 Task 3: Two Step Emotion Cause Analysis (Levchenko et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.159.pdf