  Developing semi-supervised task-oriented dialog (TOD) systems by leveraging
unlabeled dialog data has attracted increasing interests. For semi-supervised
learning of latent state TOD models, variational learning is often used, but
suffers from the annoying high-variance of the gradients propagated through
discrete latent variables and the drawback of indirectly optimizing the target
log-likelihood. Recently, an alternative algorithm, called joint stochastic
approximation (JSA), has emerged for learning discrete latent variable models
with impressive performances. In this paper, we propose to apply JSA to
semi-supervised learning of the latent state TOD models, which is referred to
as JSA-TOD. To our knowledge, JSA-TOD represents the first work in developing
JSA based semi-supervised learning of discrete latent variable conditional
models for such long sequential generation problems like in TOD systems.
Extensive experiments show that JSA-TOD significantly outperforms its
variational learning counterpart. Remarkably, semi-supervised JSA-TOD using 20%
labels performs close to the full-supervised baseline on MultiWOZ2.1.
