AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis

TL;DR

This paper introduces a structured model for extracting emotion-cause pairs from conversations, incorporating textual and multimodal cues, achieving competitive results in the SemEval-2024 Task 3.

Contribution

The paper presents a novel three-part model for emotion-cause analysis that effectively combines embedding, pair extraction, and QA-based cause identification.

Findings

Ranked 10th in subtask 1

Ranked 6th in subtask 2

Effective multimodal cause extraction

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.