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A Factored Discriminative Spoken Language Understanding for Spoken Dialogue Systems

  • Filip Jurčíček
  • Ondřej Dušek
  • Ondřej Plátek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

This paper describes a factored discriminative spoken language understanding method suitable for real-time parsing of recognised speech. It is based on a set of logistic regression classifiers, which are used to map input utterances into dialogue acts. The proposed method is evaluated on a corpus of spoken utterances from the Public Transport Information (PTI) domain. In PTI, users can interact with a dialogue system on the phone to find intra- and inter-city public transport connections and ask for weather forecast in a desired city. The results show that in adverse speech recognition conditions, the statistical parser yields significantly better results compared to the baseline well-tuned handcrafted parser.

Keywords

spoken language understanding dialogue systems meaning representation 

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References

  1. 1.
    Kate, R.J., Wong, Y.W., Mooney, R.J.: Learning to Transform Natural to Formal Languages. In: Proceedings of AAAI, pp. 1062–1068 (2005)Google Scholar
  2. 2.
    Mairesse, F., Gasic, M., Jurčíček, F., Keizer, S., Thomson, B., Yu, K., Young, S.: Spoken language understanding from unaligned data using discriminative classification models. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4749–4752 (2009)Google Scholar
  3. 3.
    Thomson, B., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Yu, K., Young, S.: User study of the Bayesian update of dialogue state approach to dialogue management. In: Proceedings of Interspeech, pp. 483–486 (2008)Google Scholar
  4. 4.
    Williams, J., Young, S.: Partially observable Markov decision processes for spoken dialog systems. Computer Speech and Language 21(2), 393–422 (2007)CrossRefGoogle Scholar
  5. 5.
    Public Transport Information System for Czech Republic (2014), https://ufal.mff.cuni.cz/alex-dialogue-systems-framework/ptics
  6. 6.
    Žilka, L., Marek, D., Korvas, M., Jurčíček, F.: Comparison of Bayesian Discriminative and Generative Models for Dialogue State Tracking. In: SIGDIAL 2013: Proc. of the 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Metz, France, pp. 452–457 (2013)Google Scholar
  7. 7.
    He, Y., Young, S.: Semantic processing using the Hidden Vector State model. Computer Speech & Language 19(1), 85–106 (2005)CrossRefGoogle Scholar
  8. 8.
    Jurčíček, F., Švec, J., Müller, L.: Extension of the HVS semantic parser by allowing left-right branching. In: Proceedings of ICASSP, pp. 4993–4996 (2008)Google Scholar
  9. 9.
    Zhu, J., Hastie, T.: Kernel logistic regression and the import vector machine. Journal of Computational and Graphical Statistics 14(1), 109–185 (2005)MathSciNetGoogle Scholar
  10. 10.
    Zettlemoyer, L.S., Collins, M.: Online learning of relaxed CCG grammars for parsing to logical form. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 678–687 (2007)Google Scholar
  11. 11.
    Dahl, D.A., Bates, M., Brown, M., Fisher, W., Hunicke-Smith, K., Pallett, D., Pao, C., Rudnicky, A., Shriberg, E.: Expanding the scope of the ATIS task: The ATIS-3 corpus. In: Proceedings of the ARPA HLT Workshop, pp. 43–48 (1994)Google Scholar
  12. 12.
    Meza-Ruiz, I.V., Riedel, S., Lemon, O.: Spoken Language Understanding in dialogue systems, using a 2-layer Markov Logic Network: Improving semantic accuracy. In: Proceedings of Londial (2008)Google Scholar
  13. 13.
    Tür, G., Hakkani-Tür, D.Z., Hillard, D., Celikyilmaz, A.: Unsupervised Spoken Language Understanding: Exploiting Query Click Logs for Slot Filling. In: Proceedings of Interspeech, pp. 1293–1296 (2011)Google Scholar
  14. 14.
    Henderson, J.: Semantic Decoder which Exploits Syntactic-Semantic Parsing, for the TownInfo Task. In: CLASSiC Project Deliverable 2.2 (2009)Google Scholar
  15. 15.
    Wong, Y.W., Mooney, R.J.: Learning for Semantic Parsing with Statistical Machine Translation. In: Proceedings of HLT/NAACL, pp. 439–446 (2006)Google Scholar
  16. 16.
    Tang, L.R., Mooney, R.J.: Using multiple clause constructors in inductive logic programming for semantic parsing. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, p. 466. Springer, Heidelberg (2001)Google Scholar
  17. 17.
    Morbini, F., Audhkhasi, K., Sagae, K., Arstein, R., Can, D., Georgiou, P.G., Narayanan, S.S., Leuski, A., Traum, D.: Which ASR should I choose for my dialogue system? In: Proc. of SIGDIAL, Metz, France, pp. 394–403 (2013)Google Scholar
  18. 18.
    Pedregosa, F., et al.: Scikit-learn: Machine Learning in Python. JMLR 12, 2825–2830 (2011)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Filip Jurčíček
    • 1
  • Ondřej Dušek
    • 1
  • Ondřej Plátek
    • 1
  1. 1.Faculty of Mathematics and Physics, Institute of Formal and Applied LinguisticsCharles University in PragueCzech Republic

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