Dual-Task Contrastive Meta-Learning for Few-Shot Cross-Domain Emotion Recognition  

作　　者：Yujiao Tang Yadong Wu Yuanmei He Jilin Liu Weihan Zhang 

机构地区：[1]School of Computer Science and Engineering,Sichuan University of Science and Engineering,Yibin,644002,China [2]School of Mechanical and Power Engineering,Chongqing University of Science and Technology,Chongqing,401331,China

出　　处：《Computers, Materials & Continua》2025年第2期2331-2352,共22页计算机、材料和连续体(英文)

基　　金：supported by the ScientificResearch and Innovation Team Program of Sichuan University of Science and Technology(No.SUSE652A006);Sichuan Key Provincial Research Base of Intelligent Tourism(ZHYJ22-03);In addition,it is also listed as a project of Sichuan Provincial Science and Technology Programme(2022YFG0028).

摘　　要：Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.

关 键 词：Contrastive learning emotion recognition cross-domain learning DUAL-TASK META-LEARNING 

分 类 号：H31[语言文字—英语]