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Conversational Agents and Negative Lessons from Behaviourism

  • Milan GnjatovićEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 159)

Abstract

This chapter addresses the question of whether it is enough to extract from data the knowledge needed to implement socially believable conversational agents. Contrary to the popular views, the answer is negative. In this respect, the chapter points to some shortcomings of fully data-driven approaches to dialogue management, including the lack of external criteria for the selection of dialogue corpora, and the misconception of dialogue structure and dialogue context. To point to these shortcomings is not to undervalue data-driven approaches, but to emphasize the message that big data provide only a partial account of human-machine dialogue, and thus must not remain wedded to small linguistic theory, as it is currently the case.

Notes

Acknowledgements

The presented study was sponsored by the Ministry of Education, Science and Technological Development of the Republic of Serbia (research grants III44008 and TR32035), and by the intergovernmental network EUREKA (research grant E!9944). The responsibility for the content of this article lies with the author.

References

  1. 1.
    Alexandersson, J., Reithinger, N.: Learning dialogue structures from a corpus. In: Proceedings of EuroSpeech-97, Rhodes, pp. 2231–2235 (1997)Google Scholar
  2. 2.
    Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  3. 3.
    Bilange, E.: An approach to oral dialogue modelling. In: Taylor, M.M., Néel, F., Bouwhuis, D.G. (eds.) The Structure of Multimodal Dialogue II, pp. 189–205. John Benjamins Publishing Company, Philadelphia/Amsterdam (2000)Google Scholar
  4. 4.
    Chomsky, N.: Language and the Cognitive Science Revolution(s) (2011). https://chomsky.info/20110408/. Cited 19 Feb 2018
  5. 5.
    Ghazvininejad, M., Brockett, C., Chang, M.-W., Dolan, B., Gao, J., Yih, W.-t., Galley, M.: A Knowledge-Grounded Neural Conversation Model, Association for the Advancement of Artificial Intelligence (2018)Google Scholar
  6. 6.
    Gnjatović, M., Borovac, B.: Toward conscious-like conversational agents. In: Toward Robotic Socially Believable Behaving Systems, Volume II—Modeling Social Signals. Esposito, A., Jain, L.C. (eds.), volume 106 of the series Intelligent Systems Reference Library, pp. 23–45. Springer (2016)Google Scholar
  7. 7.
    Gnjatović, M.: Therapist-centered design of a robot’s dialogue behavior. Cogn. Comput. 6(4), 775–788 (2014)CrossRefGoogle Scholar
  8. 8.
    Gnjatović, M., Delić, V.: Cognitively-inspired representational approach to meaning in machine dialogue. Knowl.-Based Syst. 71, 25–33 (2014)CrossRefGoogle Scholar
  9. 9.
    Gnjatović, M., Janev, M., Delić, V.: Focus tree: modeling attentional information in task-oriented human-machine interaction. Appl. Intell. 37(3), 305–320 (2012)CrossRefGoogle Scholar
  10. 10.
    Grosz, B.: Smart enough to talk with us? Foundations and challenges for dialogue capable AI systems. Comput. Linguist. 44(1), 1–15 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Grosz, B., Sidner, C.: Attention, intentions, and the structure of discourse. Comput. Linguist. 12(3), 175–204 (1986)Google Scholar
  12. 12.
    Halliday, M.A.K., Matthiessen, C.M.I.M.: An Introduction to Functional Grammar, 3rd edn. Hodder Arnold (2004)Google Scholar
  13. 13.
    Jokinen, K., McTear, M.: Spoken Dialogue Systems. Synthesis Lectures on Human Language Technologies, vol. 2(1), pp. 1–151. Morgan Claypool (2009)Google Scholar
  14. 14.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics, 2nd edn. Prentice-Hall (2009)Google Scholar
  15. 15.
    Lovász, L.: Random walks on graphs: a survey, combinatorics, Paul Erdos is eighty. Bolyai Society Mathematical Studies, vol. 2, pp. 1–46 (1993)Google Scholar
  16. 16.
    Lowe, R., Pow, N., Serban, I., Pineau, J.: The Ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. In: Special Interest Group on Discourse and Dialogue, SIGDIAL (2015)Google Scholar
  17. 17.
    Li, J., Monroe, W., Shi, T., Ritter, A., Jurafsky, D.: Adversarial Learning for Neural Dialogue Generation. Empirical Methods in Natural Language Processing (EMNLP) (2017)Google Scholar
  18. 18.
    Mikolov, T., Karafiát, M., Burget, L., Černocký, J.H., Sanjeev Khudanpur, S.: Recurrent neural network based language model. In: Proceedings of INTERSPEECH, pp. 1045–1048 (2010)Google Scholar
  19. 19.
    Mikolov, T., Yih, W.-t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL-HLT 2013, Association for Computational Linguistics, pp. 746–751 (2013)Google Scholar
  20. 20.
    Miller, G.A.: The cognitive revolution: a historical perspective. Trends Cogn. Sci. 7(3), 141–144 (2003)CrossRefGoogle Scholar
  21. 21.
    Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall (1978)Google Scholar
  22. 22.
    Ritter, A., Cherry, C., Dolan, W.B.: Data-driven response generation in social media. In: Proceedings of EMNLP 2011, pp. 583–593 (2011)Google Scholar
  23. 23.
    Ritter, A., Cherry, C., Dolan W.B.: Unsupervised modeling of twitter conversations. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT ’10, Morristown, NJ, USA, pp. 172–180 (2010)Google Scholar
  24. 24.
    Roulet, E.: On the structure of conversation as negotiation. In: Parret, H., Verschueren, J. (eds.) (On) Searle on Conversation, pp. 91–99. John Benjamins Publishing Company, Philadelphia/Amsterdam (1992)Google Scholar
  25. 25.
    Savić, S., Gnjatović, M., Mišković, D., Tasevski, J., Maček, N.: Cognitively-inspired symbolic framework for knowledge representation. In: Proceedings of the 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Debrecen, Hungary, pp. 315–320 (2017)Google Scholar
  26. 26.
    Schegloff, E.A.: Sequencing in conversational openings. Am. Anthropol. 70, 1075–1095 (1968)CrossRefGoogle Scholar
  27. 27.
    Searle, J.: Conversation. In: Parret, H., Verschueren, J. (eds.) (On) Searle on Conversation, pp. 7–29. John Benjamins Publishing Company, Philadelphia/Amsterdam (1992)Google Scholar
  28. 28.
    Serban, I.V., Lowe, R., Charlin, L., Pineau, J.: A Survey of Available Corpora for Building Data-Driven Dialogue Systems. arXiv e-prints, arXiv:1512.05742 (2015)
  29. 29.
    Serban, I.V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J: Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16, pp. 3776–3783 (2016)Google Scholar
  30. 30.
    Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1577–1586. Beijing, China (2015)Google Scholar
  31. 31.
    Shoham, Y.: Why knowledge representation matters. Commun. ACM 59(1), 47–49 (2015)CrossRefGoogle Scholar
  32. 32.
    Sinclair, J.: Corpus and text—basic principles. In: Wynne, M. (ed.) Developing Linguistic Corpora: A Guide to Good Practice, pp. 1–16. Oxbow Books, Oxford (2005)Google Scholar
  33. 33.
    Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J-Y., Gao, J., Dolan, B.: A neural network approach to context-sensitive generation of conversational responses. In: Proceedings of HLT-NAACL, pp. 196–205 (2015)Google Scholar
  34. 34.
    Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Van Ess-Dykema, C., Meteer, M.: Dialogue act modeling for automatic tagging and recognition of conversational speech. Comput. Linguist. 26(3), 339–373 (2000)CrossRefGoogle Scholar
  35. 35.
    Tognini-Bonelli, E.: Corpus Linguistics at Work. John Benjamins, Amsterdam (2001)Google Scholar
  36. 36.
    Vinyals, O., Le, Q.V.: A neural conversational model. In: ICML Deep Learning Workshop, arXiv:1506.05869 [cs.CL] (2015)
  37. 37.
    Widdowson, H.G.: On the limitations of linguistics applied. Appl. Linguist. 21(1), 3–25 (2000)CrossRefGoogle Scholar
  38. 38.
    Wilks, Y.: Is there progress on talking sensibly to machines? Science 318(5852), 927–928 (2007)CrossRefGoogle Scholar
  39. 39.
    Wilks, Y.: IR and AI: traditions of representation and anti-representation in information processing. In: McDonald, S., Tait, J. (eds.) Advances in Information Retrieval. ECIR 2004. Lecture Notes in Computer Science, vol. 2997, pp. 12–26. Springer, Berlin, Heidelberg (2004)Google Scholar
  40. 40.
    Wilks, Y., Catizone, R., Turunen, M.: Dialogue Management. COMPANIONS Consortium: State of the Art Papers (2006) Public report. https://pdfs.semanticscholar.org/fea2/03cd009a66cc232ead6b808f1c9f67c86f3f.pdf. Cited 19 Feb 2018

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Authors and Affiliations

  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia

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