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Task-oriented dialogue systems have been plagued by the difficulties ofobtaining 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 spokendialogue systems. In this paper, we propose Task-oriented Dialogue DataAugmentation (TOD-DA), a novel model-agnostic data augmentation paradigm toboost the robustness of task-oriented dialogue modeling on spokenconversations. The TOD-DA consists of two modules: 1) Dialogue Enrichment toexpand training data on task-oriented conversations for easing data sparsityand 2) Spoken Conversation Simulator to imitate oral style expressions andspeech recognition errors in diverse granularities for bridging the gap betweenwritten and spoken conversations. With such designs, our approach ranked firstin both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modelingon spoken conversations, demonstrating the superiority and effectiveness of ourproposed TOD-DA.

TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations