1 research outputs found
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
The task of dialogue generation aims to automatically provide responses given
previous utterances. Tracking dialogue states is an important ingredient in
dialogue generation for estimating users' intention. However, the
\emph{expensive nature of state labeling} and the \emph{weak interpretability}
make the dialogue state tracking a challenging problem for both task-oriented
and non-task-oriented dialogue generation: For generating responses in
task-oriented dialogues, state tracking is usually learned from manually
annotated corpora, where the human annotation is expensive for training; for
generating responses in non-task-oriented dialogues, most of existing work
neglects the explicit state tracking due to the unlimited number of dialogue
states.
In this paper, we propose the \emph{semi-supervised explicit dialogue state
tracker} (SEDST) for neural dialogue generation. To this end, our approach has
two core ingredients: \emph{CopyFlowNet} and \emph{posterior regularization}.
Specifically, we propose an encoder-decoder architecture, named
\emph{CopyFlowNet}, to represent an explicit dialogue state with a
probabilistic distribution over the vocabulary space. To optimize the training
procedure, we apply a posterior regularization strategy to integrate indirect
supervision. Extensive experiments conducted on both task-oriented and
non-task-oriented dialogue corpora demonstrate the effectiveness of our
proposed model. Moreover, we find that our proposed semi-supervised dialogue
state tracker achieves a comparable performance as state-of-the-art supervised
learning baselines in state tracking procedure