Papers by Taihao Li

3 papers
Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)

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Challenge: Existing studies focus on developing models that exploit the unification of multiple modalities.
Approach: They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities.
Outcome: The proposed model outperforms state-of-the-art models in multi-modal emotion recognition.
Amanda: Adaptively Modality-Balanced Domain Adaptation for Multimodal Emotion Recognition (2024.findings-acl)

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Challenge: Emotion recognition is a multimodal learning method that can be used for data scarcity.
Approach: They propose to use Adaptively modality-balanced domain adaptation to balance the alignment of different modalities for multimodal emotion recognition.
Outcome: The proposed model outperforms competing models on common datasets on multimodal emotion recognition.
DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations (2024.findings-emnlp)

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Challenge: Existing methods for Emotion Recognition in conversations are insufficient in understanding the rich historical emotional context.
Approach: They propose a novel model that utilizes a "recall-detect-predict" framework to imitate human emotional reasoning by 'recalling' past interactions of a speaker to collect emotional cues.
Outcome: The proposed model outperforms existing methods on three benchmark datasets and significantly outperformed existing methods.

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