Multi-domain Dialog State Tracking using Recurrent Neural Networks
This paper presents an approach to enhance dialog state tracking through the use of Recurrent Neural Networks (RNNs) across multiple domains. Dialog state tracking is essential in dialog systems for interpreting user utterances and updating belief states, which guide the system's responses. Traditionally, dialog systems are domain-specific, with models often requiring extensive in-domain data. This paper addresses the challenge of creating dialog models that operate across distinct domains, thereby advancing towards open-domain dialog systems.
The authors propose a hierarchical training procedure to construct multi-domain dialog state tracking models. This procedure entails initialising a general belief tracking model using out-of-domain data, followed by specialising the model for each domain using available domain-specific data. The approach utilises delexicalised features to facilitate transfer learning between domains and slots, allowing models to operate effectively even in domains with limited or entirely new data.
Main Contributions
- General Model Initialisation: The training process begins by using out-of-domain dialogs to initialise a generic belief tracking model. This model captures frequent and general dialog features by delexicalising slot-value pairs and slot names into generic symbols. Such features enhance transfer learning, allowing models to apply learned behaviors to slots in new domains without prior training data.
- Domain-Specific Model Specialisation: The procedure refines the initialised models for each domain by further training them with domain-specific data. The refined models retain cross-domain dialog patterns while learning domain-specific behaviors, improving performance across various dialog domains.
- Evaluation and Results: The evaluation spans six dialog domains, employing datasets from the Dialog State Tracking Challenges. The results demonstrate robust performance of multi-domain models, often surpassing domain-specific models. The geometric mean of performance across domains indicates the balance achieved by general models.
- Out-of-Domain Initialisation: The approach proves valuable for new domains with minimal training data. Models initialised with out-of-domain data consistently exhibit improved performance over purely in-domain models, indicating strong delexicalisation and domain adaptation capabilities.
Implications and Future Directions
The proposed approach mitigates the need for extensive domain-specific resources, allowing faster deployment of dialog systems across new domains. This has practical implications for developing scalable dialog systems that can adapt efficiently to varied domains. Theoretically, the method takes steps towards creating truly open-domain dialog systems.
Future work is aimed at further reducing or eliminating the requirement for annotated in-domain data to adapt models to novel domains. Exploring unsupervised domain adaptation techniques or leveraging unannotated dialogs could be potential avenues to realise this goal.
Overall, this paper provides valuable insights into multi-domain dialog state tracking and presents a scalable solution leveraging RNNs for enhanced cross-domain performance.