Modeling Semantic Relationship in Multi-turn Conversations with
Hierarchical Latent Variables
Abstract
Multi-turn conversations consist of complex
semantic structures, and it is still a challenge
to generate coherent and diverse responses
given previous utterances. It’s practical that a
conversation takes place under a background,
meanwhile, the query and response are usually most related and they are consistent in
topic but also different in content. However,
little work focuses on such hierarchical relationship among utterances. To address this
problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model
contains latent variables in three hierarchies.
The discourse-level one captures the global
background, the pair-level one stands for the
common topic information between query and
response, and the utterance-level ones try to
represent differences in content. Experimental results show that our model significantly
improves the quality of responses in terms of
fluency, coherence and diversity compared to
baseline methods