AIMA at S em E val-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis (2024)

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.

## introduction the task of emotion-cause pair extraction in conversations holds significant importance in advancing the field of emotion analysis. unlike previous endeavors that primarily focused on recognizing emotions, this task delves deeper into understanding the underlying causes behind emotional expressions within conversational contexts @xcite . recognizing that emotions are conveyed not only through words but also through vocal intonations and facial expressions, the field has shifted towards multimodal emotion recognition. this move aims to understand how emotions are interwoven with text, sound, and visual cues in dialogue @xcite . the semeval-2024 task 3 @xcite @xcite encompasses two subtasks aimed at extracting emotion-cause pairs in conversational contexts. subtask 1 focuses on textual emotion-cause pair extraction, where causes are defined and annotated as textual spans within the conversation. in contrast, subtask 2 broadens the analysis to incorporate multimodal cues, including language, audio, and vision. the task is based on the multimodal conversational emotion cause dataset ecf @xcite . figure 1 illustrates an example of the task and the annotated dataset. in this paper, we introduce an approach based on a model architecture consisting of three key components: (i) embedding extraction, (ii) cause-pair extraction & emotion classification, and (iii) cause extraction via qa post-pair detection. utilizing advanced techniques and fine-tuning on specific datasets, our goal is to dissect complex conversational dynamics and pinpoint nuances that indicate emotional causality. although our architecture supports multimodal data-including text, audio, and video through concatenations of the embeddings of these modalities using pretrained models-this study specifically harnesses textual data, as our primary focus is on addressing subtask 1. ## related work this section provides an overview of two key areas in the field of emotion analysis: emotion recognition in conversation and emotion-cause pair extraction in conversations. emotion recognition in conversation: emotion recognition in conversation, a burgeoning field, aims to decipher and understand the complex interplay of emotions within dialogues. erc has seen significant advancements in recent years (kim utterance 1 chandler: hey pheebs! utterance 2 phoebe: ohh! you made up! monica: yeah, i couldn't be mad at him for too long. ## system overview our model architecture, illustrated in figure 2 , is designed with the capacity to incorporate a diverse set of inputs from various sources such as text, video, and audio to perform emotion-cause analysis within conversational contexts. however, for the purpose of addressing subtask 1, we specifically utilized textual data. ## conclusion our investigation into emotion-cause pair extraction presents a paradigm shift towards simplicity and efficiency without compromising performance. by adopting a streamlined approach, we have demonstrated that high-impact emotion analysis does not necessarily require heavy computational resources or complex multimodal data integration. our participation in the semeval-2024 task 3 competition has validated our methodology, securing commendable rankings and highlighting the efficacy of our model. the results underscore the potential of cost-effective solutions in the realm of emotion analysis, opening doors to wider applicability in resource-constrained environments. looking forward, we aim to further optimize our model's efficiency and explore the integration of lightweight multimodal data processing techniques. this endeavor not only reinforces the viability of minimalist approaches but also sets a new benchmark for future research in emotion-cause analysis.