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Discriminative Spoken Language Understanding Using Statistical Machine Translation Alignment Models

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Artificial Intelligence and Signal Processing (AISP 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 427))

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Abstract

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.

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Notes

  1. 1.

    The first author of [4] is Christian Raymond, so we name this alignment in such way.

  2. 2.

    City names may contain more than one words and so the other labels, in this case the other words’ status will be cont, e.g. for the city name “Los Angeles”, “Los” is labeled as SR.ACity.start and “Angeles” is labeled as SR.ACity.cont.

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Mohammad Aliannejadi, Shahram Khadivi & Saeed Shiry Ghidary

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mohammad Hadi Bokaei

Authors
  1. Mohammad Aliannejadi
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  2. Shahram Khadivi
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  3. Saeed Shiry Ghidary
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  4. Mohammad Hadi Bokaei
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Corresponding author

Correspondence to Mohammad Aliannejadi .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mansour Jamzad

  3. Sharif University of Technology, Tehran, Iran

    Hossein Asadi

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© 2014 Springer International Publishing Switzerland

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Aliannejadi, M., Khadivi, S., Ghidary, S.S., Bokaei, M.H. (2014). Discriminative Spoken Language Understanding Using Statistical Machine Translation Alignment Models. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-10849-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10848-3

  • Online ISBN: 978-3-319-10849-0

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