Web: http://arxiv.org/abs/2209.07873

Sept. 19, 2022, 1:15 a.m. | Atsumoto Ohashi, Ryuichiro Higashinaka

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv language language generation natural natural language natural language generation reinforcement reinforcement learning

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