Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

2020-01-21 · Vevake Balaraman, Bernardo Magnini

In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations.

📄 PDF Abstract BibTeX arXiv:2001.07526

Code (1)

vevake/DomainAware_DST 공식 구현 pytorch

Tasks

Language ModelingLanguage Modelling Task-Oriented Dialogue Systems

Methods 이 논문이 사용한 방법론

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