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

arXiv · 2501.11170

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

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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.

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Alireza Ghahramani Kure, Mahshid Dehghani, Mohammad Mahdi Abootorabi, Nona Ghazizadeh, Seyed Arshan Dalili, Ehsaneddin Asgari. 2025-01-19. AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis. https://arxiv.org/abs/2501.11170

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