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Discriminative spoken language understanding using statistical machine translation alignment models

Aliannejadi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-319-10849-0_20
  3. Abstract:
  4. In this paper, we study the discriminative modeling of Spoken Language Understanding (SLU) using Conditional Random Fields (CRF) and Statistical Machine Translation (SMT) alignment models. Previous discriminative approaches to SLU have been dependent on n-gram features. Other previous works have used SMT alignment models to predict the output labels. We have used SMT alignment models to align the abstract labels and trained CRF to predict the labels. We show that the state transition features improve the performance. Furthermore, we have compared the proposed method with two baseline approaches; Hidden Vector States (HVS) and baseline-CRF. The results show that for the F-measure the proposed method outperforms HVS by 1.74% and baseline-CRF by 1.7% on ATIS corpus
  5. Keywords:
  6. Discriminative modeling ; Hidden vector state ; Sequential labeling ; Statistical machine translation ; Conditional random field ; Discriminative models ; Hidden vectors ; NAtural language processing ; Spoken language understanding
  7. Source: Communications in Computer and Information Science ; Vol. 427, issue , Sep , 2014 , pp. 194-202 ; ISSN: 18650929 ; ISBN: 9783319108490
  8. URL: http://link.springer.com/chapter/10.1007%2F978-3-319-10849-0_20