Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response. This allows for dedicated models for each sub-task, but we find a simple, unified approach leads to state-of-the-art performance across multiple settings on the MultiWOZ dataset. SimpleTOD is a simple approach to task-oriented dialogue that uses a single causal language model trained on all sub-tasks recast as a single sequence prediction problem. This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2. SimpleTOD improves over the prior state-of-the-art by 1.22 points in joint goal accuracy for dialogue state tracking. SimpleTOD also improves all three metrics used to evaluate action and response generation in the most complete setting for task-oriented dialog systems: inform rate by 8.1 points, success rate by 9.7 points, and BLEU by 23.5 points.

SimpleTOD采用单个因果语言模型解决任务导向的对话，具有先进的性能，并且可以完全利用预训练、开放领域的因果语言模型进行转移学习。它提高了对话状态跟踪的联合目标准确性，改善了在端到端设置中评估行动决策和响应生成的主要指标。

针对任务导向对话的简单语言模型