Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems
Rahul Goel, Shachi Paul, Tagyoung Chung, Jeremie Lecomte, Arindam, Mandal, Dilek Hakkani-Tur

TL;DR
This paper introduces a flexible, domain-agnostic dialogue state tracking framework that learns state variables independently, enabling easy expansion and integration of external resources, while maintaining competitive performance.
Contribution
The proposed framework allows for scalable, domain-independent dialogue state tracking without relying on domain-specific knowledge, facilitating easier system updates and extensions.
Findings
Achieves competitive results on DSTC2 dataset.
Enables incorporation of external resources like pre-trained embeddings.
Supports multiple values per state variable and arbitrary context features.
Abstract
Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained. It is also harder to extend such dialogue systems to different and multiple domains. The dialogue state tracker in conventional dialogue systems is one such component - it is usually designed to fit a well-defined application domain. For example, it is common for a state variable to be a categorical distribution over a manually-predefined set of entities (Henderson et al., 2013), resulting in an inflexible and hard-to-extend dialogue system. In this paper, we propose a new approach for dialogue state tracking that can generalize well over multiple domains without incorporating any domain-specific knowledge. Under…
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Taxonomy
TopicsSpeech and dialogue systems · Multi-Agent Systems and Negotiation · Topic Modeling
