Dialogue emotion recognition based on dual-graph fusion integrating time series and knowledge enhancement
Current dialogue emotion recognition research primarily focuses on modeling the dialogue context and the de-pendency relationships between speakers,while often overlooking the time series characteristics of dialogue.By using time encoding to analyze the emotional changes in conversations over time,we can better capture the trends in emotional shifts.This work introduces external knowledge as auxiliary information and designs a three-step process for knowledge selection,enabling the model to effectively choose relevant knowledge for the dialogue,thereby providing guidance for empathetic re-sponses.Additionally,a dual-graph fusion module is designed to facilitate interaction between dialogue information,impro-ving the overall performance of the model.Experiments conducted on two public datasets show that the proposed model out-performs baseline models in terms of performance metrics and overall effectiveness.
万方数据
维普
