Domain Transfer in Dialogue Systems without Turn-Level Supervision
This addresses the challenge of creating accurate dialogue systems for new domains without expensive annotations, though it is incremental as it builds on existing DST models.
The paper tackles the problem of costly turn-level annotations for dialogue state tracking in new domains by proposing a reinforcement learning method for domain transfer without such supervision, achieving performance on par with supervised models across several domains.
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation. State-of-the-art DST models are typically trained in a supervised manner from manual annotations at the turn level. However, these annotations are costly to obtain, which makes it difficult to create accurate dialogue systems for new domains. To address these limitations, we propose a method, based on reinforcement learning, for transferring DST models to new domains without turn-level supervision. Across several domains, our experiments show that this method quickly adapts off-the-shelf models to new domains and performs on par with models trained with turn-level supervision. We also show our method can improve models trained using turn-level supervision by subsequent fine-tuning optimization toward dialog-level rewards.