Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
This addresses the challenge of achieving human-like conversation in dialogue systems, but it is incremental as it builds on existing NLG methods.
The paper tackles the problem of open-ended dialogue systems by decoupling generation into planning and realization phases, showing that this approach outperforms end-to-end methods in evaluations.
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.