Domain Transfer in Dialogue Systems without Turn-Level Supervision

Abstract

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.

References in corpus (4)

• Domain Adaptation for Statistical Classifiers
• Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
• The Optimal Reward Baseline for Gradient-Based Reinforcement Learning
• Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

Cited by in corpus (1)

• Attention Guided Dialogue State Tracking with Sparse Supervision