论文标题
学习计划并分别实现开放式对话系统
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
论文作者
论文摘要
实现真正的人类进行对话的能力仍然是开放式对话系统的难以捉摸的目标。我们认为这是因为现存的自然语言产生方法（NLG）通常被解释为无法充分建模人类生成过程的端到端体系结构。为了调查，我们将生成分为两个单独的阶段：计划和实现。在计划阶段，我们培训两个计划者，以制定响应言论的计划。实现阶段使用响应计划来产生适当的响应。通过自动化和人类的严格评估，我们证明将过程与计划和实现相比，将过程分解为比端到端方法更好。
Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.
