Cross-Language Neural Dialog State Tracker for Large Ontologies Using Hierarchical Attention.

IEEE/ACM Transactions on Audio, Speech, and Language Processing(2018)

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摘要
Dialog state tracking, which refers to identifying the user intent from utterances, is one of the most important tasks in dialog management. In this paper, we present our dialog state tracker developed for the fifth dialog state tracking challenge, which focused on cross-language adaptation using a very scarce machine-translated training data when compared to the size of the ontology. Our dialog s...
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关键词
Task analysis,Ontologies,Training data,Speech processing,Predictive models,Neural networks,Vocabulary
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