CKERC : Joint Large Language Models with Commonsense Knowledge for
  Emotion Recognition in Conversation
Yumeng Fu
Emotion recognition in conversation (ERC) is a task which predicts the
emotion of an utterance in the context of a conversation. It tightly depends on
dialogue context, speaker identity information, multiparty dialogue scenario
and so on. However, the state-of-the-art method (instructERC) solely
identifying speaker, and ignores commonsense knowledge(i.e., reaction of the
listeners and intention of the speaker, etc.) behind speakers during a
conversation, which can deeply mine speaker information. To this end, we
propose a novel joint large language models with commonsense knowledge
framework for emotion recognition in conversation, namely CKERC.We design
prompts to generate interlocutors' commonsense based on historical utterances
with large language model. And we use the interlocutor commonsense
identification task for LLM pre-training to fine-tune speaker implicit clues
information.By solving above challenge, our method achieve state-of-the-art.We
extensive experiment on three widely-used datasets, i.e., IEMOCAP, MELD,
EmoryNLP, demonstrate our method superiority. Also, we conduct in-depth
analysis and further demonstrate the effectiveness of commonsense knowledge in
ERC task in large language model.