2 research outputs found
Dialogue State Induction Using Neural Latent Variable Models
Dialogue state modules are a useful component in a task-oriented dialogue
system. Traditional methods find dialogue states by manually labeling training
corpora, upon which neural models are trained. However, the labeling process
can be costly, slow, error-prone, and more importantly, cannot cover the vast
range of domains in real-world dialogues for customer service. We propose the
task of dialogue state induction, building two neural latent variable models
that mine dialogue states automatically from unlabeled customer service
dialogue records. Results show that the models can effectively find meaningful
slots. In addition, equipped with induced dialogue states, a state-of-the-art
dialogue system gives better performance compared with not using a dialogue
state module.Comment: IJCAI 202