  Structured belief states are crucial for user goal tracking and database
query in task-oriented dialog systems. However, training belief trackers often
requires expensive turn-level annotations of every user utterance. In this
paper we aim at alleviating the reliance on belief state labels in building
end-to-end dialog systems, by leveraging unlabeled dialog data towards
semi-supervised learning. We propose a probabilistic dialog model, called the
LAtent BElief State (LABES) model, where belief states are represented as
discrete latent variables and jointly modeled with system responses given user
inputs. Such latent variable modeling enables us to develop semi-supervised
learning under the principled variational learning framework. Furthermore, we
introduce LABES-S2S, which is a copy-augmented Seq2Seq model instantiation of
LABES. In supervised experiments, LABES-S2S obtains strong results on three
benchmark datasets of different scales. In utilizing unlabeled dialog data,
semi-supervised LABES-S2S significantly outperforms both supervised-only and
semi-supervised baselines. Remarkably, we can reduce the annotation demands to
50% without performance loss on MultiWOZ.
