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

• URL: http://arxiv.org/abs/2305.13199v1
• Date: Mon, 22 May 2023 16:29:20 GMT
• Title: Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
• Authors: Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng
• Abstract summary: 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. Inspired by such progress, we propose a retrieval-based method to enhance knowledge selection in TOD systems, which outperforms the traditional database query method for real-life dialogs.
• Score: 22.249113574918034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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