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.

提出一种基于检索的方法，以加强 TOD 系统中的知识选择，并结合半监督学习进行潜变量模型，该模型可以与知识检索器一起利用有标签和无标签的对话数据。实验证明，该方法在有标签和半监督设置下均优于传统的数据库查询方法。

基于半监督学习的面向知识检索任务的对话系统

Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision

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.

本文提出了一种名为 JSA-TOD 的算法，将联合随机逼近应用到半监督学习的潜在状态任务导向对话模型中，结果表明该算法在 MultiWOZ2.1 数据集上的性能显著优于基于变分学习的半监督学习算法，并在使用 20% 标签的情况下接近于完全监督学习的基准。