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Conversational Agents and Negative Lessons from Behaviourism

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Innovations in Big Data Mining and Embedded Knowledge

Part of the book series: Intelligent Systems Reference Library ((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.

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Notes

  1. 1.

    A typical architecture often found in literature contains two additional components for speech recognition and synthesis. They are omitted in Fig. 13.1, since they are not in the focus of this chapter.

  2. 2.

    The notion of a socially believable conversational agent is discussed in [6] in more detail. Here I rely on the reader’s intuitive understanding of this notion.

  3. 3.

    It should be kept in mind that the order of n-gram models in practical applications is typically not very high, due to the requirement of efficiency.

  4. 4.

    I consider here anaphoric references simply because they can be resolved within linguistic context. Exophoric references point outward from the dialogue, e.g., they may be recoverable from a spatial context. They are not included in the example in order to show that even when an element that is pointed to anaphorically (i.e., an antecedent) is explicitly stated in the dialogue, the established relation is not necessarily local.

  5. 5.

    This is actually the relation of satisfaction-precedence (cf. [11]), but the phrase “linear precedence” is used to emphasize the assumption that dialogue structure is sequential.

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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.

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Milan Gnjatović

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  1. Milan Gnjatović
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Correspondence to Milan Gnjatović .

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Editors and Affiliations

  1. Dipartimento di Psicologia and International Institute for Advanced Scientific Studies (IIASS), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Sezione di Napoli, Osservatorio Vesuviano, Istituto Nazionale di Geofisica e Vulcanologia, Napoli, Italy

    Antonietta M. Esposito

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Gnjatović, M. (2019). Conversational Agents and Negative Lessons from Behaviourism. In: Esposito, A., Esposito, A., Jain, L. (eds) Innovations in Big Data Mining and Embedded Knowledge. Intelligent Systems Reference Library, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-15939-9_13

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