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Learning to Interrupt the User at the Right Time in Incremental Dialogue Systems

  • Adam Chýlek
  • Jan Švec
  • Luboš Šmídl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)

Abstract

Continuous processing of input in incremental dialogue systems might result in the need of interrupting a user’s utterance when clarification or rapport is needed. Being able to predict the right time when to interrupt the utterance can be another step to a more human-like dialogue. On the other hand, annotation of corpora with different types of possible interruptions requires additional human resources. In this paper, we discuss how to process a corpus that does not have interruptions specifically annotated. We also present initial experiments on two corpora and show that it is possible to model the desired behaviour from these corpora.

Keywords

Incremental dialogue system Model of interruptions Corpora preparation 

References

  1. 1.
    Ward, N.G., Devault, D.: Ten challenges in highly-interactive dialog systems. In: AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, pp. 104–107 (2015)Google Scholar
  2. 2.
    Schlangen, D., Skantze, G.: A general, abstract model of incremental dialogue processing. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 710–718 (2009)Google Scholar
  3. 3.
    Walker, M., Langkilde, I., Wright, J., Gorin, A., Litman, D.: Learning to predict problematic situations in a spoken dialogue system: experiments with how may I help you? In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference, pp. 210–217 (2000)Google Scholar
  4. 4.
    Li, H.Z.: Cooperative and intrusive interruptions in inter- and intracultural dyadic discourse. J. Lang. Soc. Psychol. 20(3), 259–284 (2001)CrossRefGoogle Scholar
  5. 5.
    Skantze, G., Johansson, M., Beskow, J.: Exploring turn-taking cues in multi-party human-robot discussions about objects. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 67–74 (2014)Google Scholar
  6. 6.
    Zhao, T., Black, A.W., Eskenazi, M.: An incremental turn-taking model with active system barge-in for spoken dialog systems. In: 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 42–50, September 2015Google Scholar
  7. 7.
    Khouzaimi, H., Laroche, R., Evre, F.: Turn-taking phenomena in incremental dialogue systems. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1890–1895, September 2015Google Scholar
  8. 8.
    Heeman, P.A., Lunsford, R.: Turn-taking offsets and dialogue context. Interspeech 2017, 1671–1675 (2017)CrossRefGoogle Scholar
  9. 9.
    Masumura, R., Asami, T., Masataki, H., Ishii, R., Higashinaka, R.: Online end-of-turn detection from speech based on stacked time-asynchronous sequential networks. INTERSPEECH 2017, 1661–1665 (2017)CrossRefGoogle Scholar
  10. 10.
    Maier, A., Hough, J., Schlangen, D.: Towards deep end-of-turn prediction for situated spoken dialogue systems. INTERSPEECH 2017, 1676–1680 (2017)CrossRefGoogle Scholar
  11. 11.
    Gravano, A., Hirschberg, J.: A corpus-based study of interruptions in spoken dialogue. In: INTERSPEECH-2012 (2012)Google Scholar
  12. 12.
    Psutka, J., Radová, V., Ircing, P., Matoušek, J., Müller, L.: USC-SFI MALACH Interviews and Transcripts Czech LDC2014S04 (2014)Google Scholar
  13. 13.
    Valenta, T., Šmídl, L., Švec, J., Soutner, D.: Inter-annotator agreement on spontaneous Czech language. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2014. LNCS (LNAI), vol. 8655, pp. 390–397. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10816-2_47CrossRefGoogle Scholar
  14. 14.
    Canavan, A., Zipperlen, G.: CALLFRIEND American English-non-southern dialect. Linguist. Data Consort. Phila. 10, 1 (1996)Google Scholar
  15. 15.
    Schuller, B., Steidl, S., Batliner, A.: The INTERSPEECH 2009 emotion challenge. In: Interspeech, pp. 312-315 (2009)Google Scholar
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Multimed. Tools Appl. 1–17 (2015)Google Scholar
  17. 17.
    Xiong,W., et al.: The Microsoft 2016 conversational speech recognition system. In: ICASSP, pp. 5255-5259 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.NTIS – New Technologies for Information Society, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

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