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- W2034361844 abstract "This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domain-dependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems." @default.
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- W2034361844 date "2009-05-01" @default.
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- W2034361844 title "Multi-domain spoken language understanding with transfer learning" @default.
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- W2034361844 doi "https://doi.org/10.1016/j.specom.2009.01.001" @default.
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