Abstract: Emotion recognition in conversation aims to classify the emotion of each utterance within a conversation. However, the label distribution in most datasets often exhibits significant imbalance. To address this issue, a data augmentation approach is proposed to enhance the model performance under conditions of label imbalance. Specifically, large language models are utilized to generate additional samples through back-translation, paraphrasing, and dialogue generation. The samples are filtered based on the harmonic mean of cosine similarity and self-Levenshtein distance. Experimental results on many  datasets show that this method improves model performance in imbalanced datasets, achieving gains in weighted-F1 scores and the recognition of minority labels compared to other state-of-the-art models.

Key words: data augmentation, emotion recognition in conversation, large language models