AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
Alireza Ghahramani Kure, Mahshid Dehghani, Mohammad Mahdi Abootorabi, Nona Ghazizadeh, Seyed Arshan Dalili, Ehsaneddin Asgari
Abstract
The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction & emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.- Anthology ID:
- 2024.semeval-1.243
- 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:
- 1698–1703
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.243
- DOI:
- 10.18653/v1/2024.semeval-1.243
- Cite (ACL):
- Alireza Ghahramani Kure, Mahshid Dehghani, Mohammad Mahdi Abootorabi, Nona Ghazizadeh, Seyed Arshan Dalili, and Ehsaneddin Asgari. 2024. AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1698–1703, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis (Ghahramani Kure et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.semeval-1.243.pdf