User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on handcrafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Crossmodel evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.
REF introduced the Neural User Simulator (NUS) which learned user behaviour from a corpus and generates natural language directly instead of semantic output such as dialog acts.
Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Computer Science Mathematics