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Recent research has shown that multi-task pre-training greatly improves themodel's robustness and transfer ability, which is crucial for building ahigh-quality dialog system. However, most previous works on multi-taskpre-training rely heavily on human-defined input format or prompt, which is notoptimal in quality and quantity. In this work, we propose to use Task-basedAutomatic Prompt generation (TAP) to automatically generate high-qualityprompts. Using the high-quality prompts generated, we scale the corpus of thepre-trained conversation model to 122 datasets from 15 dialog-related tasks,resulting in Universal Pre-trained Conversation Model (UniPCM), a powerfulfoundation model for various conversational tasks and different dialog systems.Extensive experiments have shown that UniPCM is robust to input prompts andcapable of various dialog-related tasks. Moreover, UniPCM has strong transferability and excels at low resource scenarios, achieving SOTA results on 9different datasets ranging from task-oriented dialog to open-domainconversation. Furthermore, we are amazed to find that TAP can generate promptson par with those collected with crowdsourcing. The code is released with thepaper.

UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt