Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering

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 MultiWOZ 2.1 datasets, respectively. Additionally, our model consistently outperforms the state-of-the-art model in domain adaptation settings. (Code is released at https://github.com/alexa/dstqa )