Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition

Cam-Van Thi Nguyen, Cao-Bach Nguyen, Duc-Trong Le, Quang-Thuy Ha

Abstract
Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Curriculum learning facilitates the learning process by gradually presenting training samples in a meaningful order, thereby improving the model’s performance in handling emotional variations and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models. We release the code for and experiments: https://github.com/vanntc711/MultiDAG-CL.
Anthology ID:
2024.lrec-main.380
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
4259–4265
Language:
URL:
https://aclanthology.org/2024.lrec-main.380
DOI:
Bibkey:
Cite (ACL):
Cam-Van Thi Nguyen, Cao-Bach Nguyen, Duc-Trong Le, and Quang-Thuy Ha. 2024. Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4259–4265, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (Nguyen et al., LREC-COLING 2024)
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