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Building user simulators (USs) for reinforcement learning (RL) oftask-oriented dialog systems (DSs) has gained more and more attention, which,however, still faces several fundamental challenges. First, it is unclearwhether we can leverage pretrained language models to design, for example,GPT-2 based USs, to catch up and interact with the recently advanced GPT-2based DSs. Second, an important ingredient in a US is that the user goal can beeffectively incorporated and tracked; but how to flexibly integrate goal statetracking and develop an end-to-end trainable US for multi-domains has remainedto be a challenge. In this work, we propose a generative user simulator (GUS)with GPT-2 based architecture and goal state tracking towards addressing theabove two challenges. Extensive experiments are conducted on MultiWOZ2.1.Different DSs are trained via RL with GUS, the classic agenda-based usersimulator (ABUS) and other ablation simulators respectively, and are comparedfor cross-model evaluation, corpus-based evaluation and human evaluation. TheGUS achieves superior results in all three evaluation tasks.

A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems