  When a natural language generation (NLG) component is implemented in a
real-world task-oriented dialogue system, it is necessary to generate not only
natural utterances as learned on training data but also utterances adapted to
the dialogue environment (e.g., noise from environmental sounds) and the user
(e.g., users with low levels of understanding ability). Inspired by recent
advances in reinforcement learning (RL) for language generation tasks, we
propose ANTOR, a method for Adaptive Natural language generation for
Task-Oriented dialogue via Reinforcement learning. In ANTOR, a natural language
understanding (NLU) module, which corresponds to the user's understanding of
system utterances, is incorporated into the objective function of RL. If the
NLG's intentions are correctly conveyed to the NLU, which understands a
system's utterances, the NLG is given a positive reward. We conducted
experiments on the MultiWOZ dataset, and we confirmed that ANTOR could generate
adaptive utterances against speech recognition errors and the different
vocabulary levels of users.
