Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization
Guiyang Hou, Yongliang Shen, Wenqi Zhang, Wei Xue, Weiming Lü · 2023
Emotion recognition in conversation (ERC) has attracted increasing attention in natural language processing community.Previous work commonly first extract semantic-view features via fine-tuning pre-trained language models (PLMs), then models context-view features based on the obtained semantic-view features by various graph neural networks.However, it is difficult to fully model interaction between utterances simply through a graph neural network and the features at semantic-view and context-view are not well aligned.Moreover, the previous parametric learning paradigm struggle to learn the patterns of tail class given fewer instances.To this end, we treat the pretrained conversation model as a prior knowledge base and from which we elicit correlations between utterances by a probing procedure.And we adopt supervised contrastive learning to align semantic-view and context-view features, these two views of features work together in a complementary manner, contributing to ERC from distinct perspectives.Meanwhile, we propose a new semi-parametric paradigm of inferencing through memorization to solve the recognition problem of tail class samples.We consistently achieve state-of-the-art results on four widely used benchmarks.Extensive experiments demonstrate the effectiveness of our proposed multi-view feature alignment and memorization 1 .