Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Li Zhou, Kevin Small

TL;DR
This paper introduces DSTQA, a question answering approach for multi-domain dialogue state tracking that uses a dynamic knowledge graph to improve generalization to unseen domains, slots, and values.
Contribution
It models dialogue state tracking as a question answering task with a dynamic knowledge graph, enabling better generalization to new domains and slots.
Findings
Achieves 5.80% and 12.21% relative improvements on MultiWOZ datasets.
Outperforms state-of-the-art in domain adaptation.
Uses a dynamic knowledge graph to model relationships.
Abstract
Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems. The domain ontology (i.e., specification of domains, slots, and values) of a conversational AI system is generally incomplete, making the capability for DST models to generalize to new slots, values, and domains during inference imperative. In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA). Within DSTQA, each turn generates a question asking for the value of a (domain, slot) pair, thus making it naturally extensible to unseen domains, slots, and values. Additionally, we use a dynamically-evolving knowledge graph to explicitly learn relationships between (domain, slot) pairs. Our model has a 5.80% and 12.21% relative improvement over the current state-of-the-art model on MultiWOZ 2.0 and…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
MethodsDynamic Sparse Training
