Abstract
In this paper, we present our submission to the SemEval-2023 Task 3 “The Competition of Multimodal Emotion Cause Analysis in Conversations”, focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and using a BiLSTM-based neural network to detect causes. We score 2nd in the ranking for Subtask 1, demonstrating the effectiveness of our approach through one of the highest weighted-average proportional F1 scores recorded at 0.264.- Anthology ID:
- 2024.semeval-1.164
- 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:
- 1127–1134
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.164
- DOI:
- Cite (ACL):
- Roman Kazakov, Kseniia Petukhova, and Ekaterina Kochmar. 2024. PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1127–1134, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations (Kazakov et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.164.pdf