MULTI-DOMAIN SPOKEN LANGUAGE UNDERSTANDING WITH TRANSFER LEARNING
SCIE
SCOPUS
- Title
- MULTI-DOMAIN SPOKEN LANGUAGE UNDERSTANDING WITH TRANSFER LEARNING
- Authors
- Minwoo Jeong; Lee, GG
- Date Issued
- 2009-05
- Publisher
- ELSEVIER SCIENCE BV
- 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. (C) 2009 Elsevier B.V. All rights reserved.
- Keywords
- Spoken language understanding; Multi-domain dialog system; Transfer learning; Triangular-chain structure model; SEMANTIC ROLES; DIALOGUE; SYSTEM
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/27747
- DOI
- 10.1016/J.SPECOM.2009.01.001
- ISSN
- 0167-6393
- Article Type
- Article
- Citation
- SPEECH COMMUNICATION, vol. 51, no. 5, page. 412 - 424, 2009-05
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