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About this paper
Qianrui Zhou, Hua Xu, Hao Li, Hanlei Zhang, Xiaohan Zhang, Yifan Wang, Kai Gao
Researchers from Tsinghua University and Hebei University of Science and Technology present TCL-MAP, a framework that leverages token-level contrastive learning and modality-aware prompting to improve multimodal intent recognition. This approach effectively enhances inter-modality correlation learning and semantic feature extraction from nonverbal data, achieving up to 1.12% higher accuracy on the MELD-DA dataset compared to existing state-of-the-art methods.
Key insights
- Dynamically generated, instance-conditional modality-aware prompts, which integrate nonverbal cues, significantly enhance the semantic representation of text for more accurate intent recognition.
- Guiding semantic feature learning in nonverbal modalities with strong supervisory signals from ground truth intent labels through token-level contrastive learning effectively extracts deep, intent-relevant information.
- A similarity-based modality alignment mechanism is crucial for reducing discrepancies between nonverbal modalities and learnable tokens, facilitating effective multimodal feature integration.
Summary by alphaXiv.
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