DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable
  Task-Oriented Dialogue Systems
Qingyang Wu James Gung Raphael Shu Yi Zhang
Dialogue act annotations are important to improve response generation quality
in task-oriented dialogue systems. However, it can be challenging to use
dialogue acts to control response generation in a generalizable way because
different datasets and tasks may have incompatible annotations. While
alternative methods that utilize latent action spaces or reinforcement learning
do not require explicit annotations, they may lack interpretability or face
difficulties defining task-specific rewards. In this work, we present a novel
end-to-end latent dialogue act model (DiactTOD) that represents dialogue acts
in a latent space. DiactTOD, when pre-trained on a large corpus, is able to
predict and control dialogue acts to generate controllable responses using
these latent representations in a zero-shot fashion. Our approach demonstrates
state-of-the-art performance across a wide range of experimental settings on
the MultiWOZ dataset, including zero-shot, few-shot, and full data fine-tuning
with both end-to-end and policy optimization configurations.