Task-oriented dialogue is often decomposed into three tasks: understanding
user input, deciding actions, and generating a response. While such
decomposition might suggest a dedicated model for each sub-task, we find a
simple, unified approach leads to state-of-the-art performance 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 in joint goal accuracy for
dialogue state tracking, and our analysis reveals robustness to noisy
annotations in this setting. SimpleTOD also improves the main metrics used to
evaluate action decisions and response generation in an end-to-end setting:
inform rate by 8.1 points, success rate by 9.7 points, and combined score by
7.2 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。