Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition
Yige Xu, Zhiwei Zeng, Zhiqi Shen · 2023
Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines.The rise of pre-trained language models (PLMs) has further pushed the limit of ERC performance.However, most recent works on ERC using PLMs are heavily datadriven and require fine-tuning the entire PLMs.To improve both sample and computational efficiency, we propose a derivative-free optimization method called Cross-Task Prompt Tuning (CTPT) for few-shot conversational emotion recognition.Unlike existing methods that learn independent knowledge from individual tasks, CTPT leverages sharable crosstask knowledge by exploiting external knowledge from other source tasks to improve learning performance under the few-shot setting.Moreover, CTPT only needs to optimize a vector under the low intrinsic dimensionality without gradient, which is highly training-efficient compared with existing approaches.Experiments on five different contextual conversation datasets demonstrate that our CTPT method has superior results on both few-shot scenarios and zero-shot transfers.