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Spoken language understanding (SLU) system usually consists of various pipeline components, where each component heavily relies on the results of its upstream ones.
Natural language understanding
James Allen. 1988 · 1988
Earlier work this paper cites.
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Ye-Yi Wang, Li Deng, and Alex Acero. 2005 · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Martin Zinkevich, Markus Weimer, Alexander J Smola, and Lihong Li. 2010 · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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