On the Effectiveness of Offline RL for Dialogue Response Generation
This work addresses the challenge of generating diverse and meaningful dialogue responses for natural language processing applications, but it is incremental as it builds on existing offline RL techniques.
The paper tackled the problem of dialogue response generation by evaluating offline reinforcement learning methods to maximize sequence-level objectives, finding clear performance improvements over teacher forcing without training instability or increased budgets.
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different ways. This motivates use of sequence-level objectives for dialogue response generation. In this paper, we study the efficacy of various offline reinforcement learning (RL) methods to maximize such objectives. We present a comprehensive evaluation across multiple datasets, models, and metrics. Offline RL shows a clear performance improvement over teacher forcing while not inducing training instability or sacrificing practical training budgets.