A Simple Language Model for Task Oriented Dialogue
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 by 0.49 points in joint goal accuracy for dialogue state tracking. More impressively, SimpleTOD also improves the main metrics used to evaluate action decisions and response generation in an end to end setting for task oriented dialog systems: inform rate by 8.1 points, success rate by 9.7 points, and combined score by 7.2 points.
NeurIPS