MA-DST: Multi-Attention Based Scalable Dialog State Tracking
arXiv:2002.08898
Abstract
Task oriented dialog agents provide a natural language interface for users to complete their goal. Dialog State Tracking (DST), which is often a core component of these systems, tracks the system's understanding of the user's goal throughout the conversation. To enable accurate multi-domain DST, the model needs to encode dependencies between past utterances and slot semantics and understand the dialog context, including long-range cross-domain references. We introduce a novel architecture for this task to encode the conversation history and slot semantics more robustly by using attention mechanisms at multiple granularities. In particular, we use cross-attention to model relationships between the context and slots at different semantic levels and self-attention to resolve cross-domain coreferences. In addition, our proposed architecture does not rely on knowing the domain ontologies beforehand and can also be used in a zero-shot setting for new domains or unseen slot values. Our model improves the joint goal accuracy by 5% (absolute) in the full-data setting and by up to 2% (absolute) in the zero-shot setting over the present state-of-the-art on the MultiWoZ 2.1 dataset.
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
References in corpus (4)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
- Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
- Dialog State Tracking: A Neural Reading Comprehension Approach
Cited by in corpus (5)
- CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
- Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
- Zero-Shot Dialogue State Tracking via Cross-Task Transfer
- Improving Limited Labeled Dialogue State Tracking with Self-Supervision
- An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction