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The underlying difference of linguistic patterns between general text andtask-oriented dialogue makes existing pre-trained language models less usefulin practice. In this work, we unify nine human-human and multi-turntask-oriented dialogue datasets for language modeling. To better model dialoguebehavior during pre-training, we incorporate user and system tokens into themasked language modeling. We propose a contrastive objective function tosimulate the response selection task. Our pre-trained task-oriented dialogueBERT (TOD-BERT) outperforms strong baselines like BERT on four downstreamtask-oriented dialogue applications, including intention recognition, dialoguestate tracking, dialogue act prediction, and response selection. We also showthat TOD-BERT has a stronger few-shot ability that can mitigate the datascarcity problem for task-oriented dialogue.

TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue