Contextual Slot Carryover for Disparate Schemas

In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges...

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Bibliographic Details
Published in:arXiv.org 2018-06
Main Authors: Naik, Chetan, Gupta, Arpit, Ge, Hancheng, Lambert, Mathias, Sarikaya, Ruhi
Format: Article
Language:English
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Neural networks
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Summary:In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges - scaling to a very large and potentially unbounded set of slot values, and dealing with diverse schemas. We present a neural network architecture that addresses the slot value scalability challenge by reformulating the contextual interpretation as a decision to carryover a slot from a set of possible candidates. To deal with heterogenous schemas, we introduce a simple data-driven method for trans- forming the candidate slots. Our experiments show that our approach can scale to multiple domains and provides competitive results over a strong baseline.
ISSN:2331-8422
DOI:10.48550/arxiv.1806.01773