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• W2962715022 abstract "In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain. The pipeline approach, however, has some disadvantages: error propagation and lack of information sharing. To address these issues, we present a unified neural network that jointly performs domain, intent, and slot predictions. Our approach adopts a principled architecture for multitask learning to fold in the state-of-the-art models for each task. With a few more ingredients, e.g. orthography-sensitive input encoding and curriculum training, our model delivered significant improvements in all three tasks across all domains over strong baselines, including one using oracle prediction for domain detection, on real user data of a commercial personal assistant." @default.
• W2962715022 created "2019-07-30" @default.
• W2962715022 creator A5010305620 @default.
• W2962715022 creator A5059051692 @default.
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• W2962715022 date "2017-12-01" @default.
• W2962715022 modified "2023-09-25" @default.
• W2962715022 title "ONENET: Joint domain, intent, slot prediction for spoken language understanding" @default.
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• W2962715022 doi "https://doi.org/10.1109/asru.2017.8268984" @default.
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