  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.
