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.

翻译：当前多数任务导向对话系统通过追踪槽位与值的对话状态，并基于该状态查询数据库获取相关知识以生成回复。在实际应用中，用户话语噪声较大，导致精准追踪对话状态和正确获取相关知识更加困难。近年来，问答系统与文档接地对话系统采用检索增强方法并配备知识检索器取得了进展。受此启发，我们提出一种基于检索的方法来增强任务导向型对话系统的知识选择能力，该方法在处理真实对话场景时显著优于传统数据库查询方法。进一步地，我们开发了基于潜变量模型的半监督学习方法，该方法可与知识检索器协同工作，同时利用已标注和未标注的对话数据。采用联合随机逼近算法进行半监督模型训练，整体系统命名为JSA-KRTOD。在中国移动客服真实数据集MobileCS上的实验表明，JSA-KRTOD在纯标注训练和半监督训练两种设定下均取得了优越性能。