GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings.
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Datasets
DailyDialog
MultiWOZ
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| End-To-End Dialogue Modelling | MULTIWOZ 2.0 | GALAXY | MultiWOZ (Success) | 85.3 | # 1 | |
| MultiWOZ (Inform) | 94.4 | # 1 | ||||
| BLEU | 20.5 | # 2 | ||||
| End-To-End Dialogue Modelling | MULTIWOZ 2.1 | GALAXY | MultiWOZ (Success) | 86.20 | # 1 | |
| MultiWOZ (Inform) | 95.30 | # 1 | ||||
| BLEU | 20.01 | # 1 |
