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One major task of spoken language understanding (SLU) in modern personal assistants is to extract semantic concepts from an utterance, called slot filling.
The atis spoken language systems pilot corpus
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Using recurrent neural networks for slot filling in spoken language understanding
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Slot-gated modeling for joint slot filling and intent prediction
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Leveraging sentence-level information with encoder lstm for semantic slot filling
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