Task-oriented dialogue systems have been plagued by the difficulties of
obtaining large-scale and high-quality annotated conversations. Furthermore,
most of the publicly available datasets only include written conversations,
which are insufficient to reflect actual human behaviors in practical spoken
dialogue systems. In this paper, we propose Task-oriented Dialogue Data
Augmentation (TOD-DA), a novel model-agnostic data augmentation paradigm to
boost the robustness of task-oriented dialogue modeling on spoken
conversations. The TOD-DA consists of two modules: 1) Dialogue Enrichment to
expand training data on task-oriented conversations for easing data sparsity
and 2) Spoken Conversation Simulator to imitate oral style expressions and
speech recognition errors in diverse granularities for bridging the gap between
written and spoken conversations. With such designs, our approach ranked first
in both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modeling
on spoken conversations, demonstrating the superiority and effectiveness of our
proposed TOD-DA.

本文提出了一种新的任务定向对话数据增强方法 TOD-DA，它包括对话丰富和口语会话模拟器，可以增加大规模高质量带注释口语会话，以提高口语任务定向对话建模的鲁棒性。在 DSTC10 Track2 的两项任务中，我们的方法均排名第一，证明了 TOD-DA 的优越性和有效性。