Incremental Encoding Transformer Incorporating Common-sense Awareness for Conversational Sentiment Recognition

Abstract

Conversational sentiment recognition has been widely used in people's lives and work. However, machines do not understand emotions through common-sense cognition. We propose an Incremental Encoding Transformer Incorporating Common-sense Awareness (IETCA) model. The model helps the machines use common-sense knowledge to better understand emotions in conversation. The model uses a context-aware graph attention mechanism to obtain knowledge-rich utterance representations and uses an incremental encoding Transformer to get rich contextual representations. We do some experiments on five datasets. The results show that the model has some improvement in conversational sentiment recognition.