TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
Chien-Sheng Wu, Steven C. H. Hoi, Richard Socher, Caiming Xiong · 2020
The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling.To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.We propose a contrastive objective function to simulate the response selection task.Our pre-trained task-oriented dialogue BERT (TOD-BERT) outperforms strong baselines like BERT on four downstream taskoriented dialogue applications, including intention recognition, dialogue state tracking, dialogue act prediction, and response selection.We also show that TOD-BERT has a stronger few-shot ability that can mitigate the data scarcity problem for task-oriented dialogue.