Abstract
This paper presents the solution of the DeepPavlov team for the Multimodal Sentiment Cause Analysis competition in SemEval-2024 Task 3, Subtask 2 (Wang et al., 2024). In the evaluation leaderboard, our approach ranks 7th with an F1-score of 0.2132. Large Language Models (LLMs) are transformative in their ability to comprehend and generate human-like text. With recent advancements, Multimodal Large Language Models (MLLMs) have expanded LLM capabilities, integrating different modalities such as audio, vision, and language. Our work delves into the state-of-the-art MLLM Video-LLaMA, its associated modalities, and its application to the emotion reasoning downstream task, Multimodal Emotion Cause Analysis in Conversations (MECAC). We investigate the model’s performance in several modes: zero-shot, few-shot, individual embeddings, and fine-tuned, providing insights into their limits and potential enhancements for emotion understanding.- Anthology ID:
- 2024.semeval-1.249
- 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:
- 1747–1757
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.249
- DOI:
- Cite (ACL):
- Julia Belikova and Dmitrii Kosenko. 2024. DeepPavlov at SemEval-2024 Task 3: Multimodal Large Language Models in Emotion Reasoning. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1747–1757, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- DeepPavlov at SemEval-2024 Task 3: Multimodal Large Language Models in Emotion Reasoning (Belikova & Kosenko, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.249.pdf