Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for
  Counselor Reflection Generation
Do June Min Veronica Perez-Rosas Kenneth Resnicow Rada Mihalcea
In this paper, we study the problem of multi-reward reinforcement learning to
jointly optimize for multiple text qualities for natural language generation.
We focus on the task of counselor reflection generation, where we optimize the
generators to simultaneously improve the fluency, coherence, and reflection
quality of generated counselor responses. We introduce two novel bandit
methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining
rewards into a single value and optimizing them simultaneously. Specifically,
we employ non-contextual and contextual multi-arm bandits to dynamically adjust
multiple reward weights during training. Through automatic and manual
evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt,
outperform existing naive and bandit baselines, showcasing their potential for
enhancing language models.