DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented
  Dialogue Representations
Weihao Zeng Dayuan Fu Keqing He Yejie Wang Yukai Xu Weiran Xu
Language models pre-trained on general text have achieved impressive results
in diverse fields. Yet, the distinct linguistic characteristics of
task-oriented dialogues (TOD) compared to general text limit the practical
utility of existing language models. Current task-oriented dialogue
pre-training methods overlook the one-to-many property of conversations, where
multiple responses can be appropriate given the same conversation context. In
this paper, we propose a novel dialogue pre-training model called DivTOD, which
collaborates with LLMs to learn diverse task-oriented dialogue representations.
DivTOD guides LLMs in transferring diverse knowledge to smaller models while
removing domain knowledge that contradicts task-oriented dialogues. Experiments
show that our model outperforms strong TOD baselines on various downstream
dialogue tasks and learns the intrinsic diversity of task-oriented dialogues.