SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition


  • Wei Li Nanyang Technological University, Singapore
  • Luyao Zhu Nanyang Technological University, Singapore
  • Rui Mao Nanyang Technological University, Singapore
  • Erik Cambria Nanyang Technological University, Singapore



SNLP: Applications, SNLP: Sentiment Analysis and Stylistic Analysis


Emotion recognition in conversation (ERC) has received increasing attention from the research community. However, the ERC task is challenging, largely due to the complex and unstructured properties of multi-party conversations. Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. We first introduce a dialogue parsing algorithm into ERC and further improve the algorithm through a transfer learning method. Moreover, we leverage different symbolic knowledge graph relations to learn knowledge-enhanced features for the ERC task. Extensive experiments on three benchmarks demonstrate that both dialogue structure graphs and symbolic knowledge are beneficial to the model performance on the task. Additionally, experimental results indicate that the proposed model surpasses baseline models on several indices.




How to Cite

Li, W., Zhu, L., Mao, R., & Cambria, E. (2023). SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13121-13129.



AAAI Technical Track on Speech & Natural Language Processing