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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.17269 |
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| _version_ | 1866913257515646976 |
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| author | Nguyen, Cam-Van Thi Nguyen, Cao-Bach Ha, Quang-Thuy Le, Duc-Trong |
| author_facet | Nguyen, Cam-Van Thi Nguyen, Cao-Bach Ha, Quang-Thuy Le, Duc-Trong |
| contents | 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 MultiDAG+CL and experiments: https://github.com/vanntc711/MultiDAG-CL |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition Nguyen, Cam-Van Thi Nguyen, Cao-Bach Ha, Quang-Thuy Le, Duc-Trong Machine Learning 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 MultiDAG+CL and experiments: https://github.com/vanntc711/MultiDAG-CL |
| title | Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.17269 |