Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

ACL 2018

Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

Jan 28, 2021
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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, se-mantically 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 single-domain dialogue tracking tasks. Authors: Osman Ramadan, Paweł Budzianowski, Milica Gašić (University of Cambridge)

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