Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation
This addresses the problem of recognizing emotions in conversations lacking explicit expressions, which is important for applications like chatbots and social analysis, though it appears incremental as it builds on existing prompt-tuning paradigms.
The authors tackled emotion recognition in conversations (ERC) by proposing CISPER, a model that uses prompts blending contextual information and commonsense to better leverage pre-trained language models, achieving superior performance over state-of-the-art models.
Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning. Specifically, CISPER is equipped with the prompt blending the contextual information and commonsense related to the interlocutor's utterances, to achieve ERC more effectively. Our extensive experiments demonstrate CISPER's superior performance over the state-of-the-art ERC models, and the effectiveness of leveraging these two kinds of significant prompt information for performance gains. To reproduce our experimental results conveniently, CISPER's sourcecode and the datasets have been shared at https://github.com/DeqingYang/CISPER.