An emotion classification method based on mixed expert model and large model cooperation

Abstract

This invention belongs to the field of natural language processing and dialogue emotion recognition, specifically involving an emotion classification method based on a hybrid expert model and a large-scale model collaboration. The method first encodes the dialogue text to construct basic features. Then, it extracts semantic, contextual, and knowledge-enhanced features through heterogeneous expert networks, and uses a dynamic routing gating network to weightedly fuse the features output by different experts, forming a unified emotion feature representation. Next, this feature representation is mapped to a soft cue vector, concatenated with the word embedding sequence of the original text, and input into a pre-trained large-scale model for emotion inference. During the training phase, cross-entropy loss is used as the target, updating only the parameters of the expert network, routing gating network, and soft cue mapping network, achieving efficient end-to-end parameter training. This invention significantly improves the accuracy of emotion classification by fusing multi-dimensional features and combining the common-sense reasoning ability of a large-scale model, while also achieving higher training efficiency.

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