KnowDT: Empathetic dialogue generation with knowledge enhanced dependency tree

A human-like dialogue system should prioritize expressing empathy towards others, which entails two crucial aspects: (1) semantic cognition and (2) emotion detection. Previous approaches mainly model semantic and emotional dependencies by capturing emotion signal or leveraging external commonsense knowledge. However, they often ignore the syntactic information inherent in the utterances, which is crucial to understanding the subtle differences in the semantic and emotional meaning of words in different contexts. In this paper, we propose a novel framework for empathetic dialogue generation with Knowledge enhanced Dependency Tree (KnowDT). Specifically, the KnowDT captures relationships between token-level emotions by comprehensively considering of dependency tree and external knowledge. To incorporate syntactic and semantic information into context encoding, we improve the relation attention mechanism for a joint representation of adjacent nodes in dependency tree, and design a flexible tree positional encoding scheme for a broad variety of topologies without affecting their syntactic constituents. Furthermore, we present a level-ordered attention mechanism that extracts emotional features by encoding recursively along the topological structure of a dependency tree. Experimental results demonstrate that our model outperforms the state-of-the-art models in both automatic and human evaluation, in which the accuracy of emotion classification and the perplexity of generated responses are significantly improved.