Year: 2018
Type: conference-paper
Abstract: Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems.The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues.However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology.In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains.The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora.Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in singledomain dialogue tracking tasks.
DOI: 10.18653/v1/p18-2069
Institution: University of Cambridge
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2798434138