Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
Yucheng Cai Hong Liu Zhijian Ou Yi Huang Junlan Feng
Most existing task-oriented dialog (TOD) systems track dialog states in terms
of slots and values and use them to query a database to get relevant knowledge
to generate responses. In real-life applications, user utterances are noisier,
and thus it is more difficult to accurately track dialog states and correctly
secure relevant knowledge. Recently, a progress in question answering and
document-grounded dialog systems is retrieval-augmented methods with a
knowledge retriever. Inspired by such progress, we propose a retrieval-based
method to enhance knowledge selection in TOD systems, which significantly
outperforms the traditional database query method for real-life dialogs.
Further, we develop latent variable model based semi-supervised learning, which
can work with the knowledge retriever to leverage both labeled and unlabeled
dialog data. Joint Stochastic Approximation (JSA) algorithm is employed for
semi-supervised model training, and the whole system is referred to as that
JSA-KRTOD. Experiments are conducted on a real-life dataset from China Mobile
Custom-Service, called MobileCS, and show that JSA-KRTOD achieves superior
performances in both labeled-only and semi-supervised settings.