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Spoken language understanding (SLU) systems use various features to detect the domain, intent and semantic slots of a query. In addition to n-grams, features generated from entity dictionaries are often used in model training. Clean or properly weighted dictionaries are critical to improve model's coverage and accuracy for unseen entities during test time. However, clean dictionaries are hard to obtain...
This paper proposes a new technique to enable Natural Language Understanding (NLU) systems to handle user queries beyond their original semantic schemas defined by intents and slots. Knowledge graph and search query logs are used to extend NLU system's coverage by transferring intents from other domains to a given domain. The transferred intents as well as existing intents are then applied to a set...
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