Conceptual alignment in reference with artificial and human dialogue partners

Conceptual alignment in reference with artificial and human dialogue partners Koen van Lierop (koenvanlierop@gmail.com) Martijn Goudbeek (m.b.goudbeek@uvt.nl) Emiel Krahmer (e.j.krahmer@uvt.nl) Tilburg center for Cognition and Communication (TiCC), Faculty of Humanities, Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands Abstract Previous work on reference in dialogue has shown that speakers adapt to the concepts that were used in earlier references during an interaction (such as orientation when a dialogue partner describes a chair as “the chair seen from the front”), even if these concepts are generally dispreferred. Here, we investigate to what extent it matters whether speakers interact with an artificial or a human dialogue partner (Study 1) and whether this adaptation indeed takes place at the conceptual level (Study 2). For Study 1 participants interacted either with a computer or with a human confederate and it was found that participants adapt in similar ways and just as much to a human dialogue partner as to a computer. Study 2 used a cross-language interaction paradigm, in which bilingual participants listened to English descriptions after which they had to refer in Dutch (thereby reducing the possibilities for lexical and syntactic alignment). The results showed that even with crosslinguistic prime-target pairings, participants aligned with the attributes used by their dialogue partner, providing further evidence for alignment at the conceptual level. Keywords: referring expressions, alignment, computer interaction, conceptual alignment human- Introduction During conversations, people continuously refer to other people, objects or events, for example using descriptions such as the man with the beard or the blue chair. Since such descriptions are so common (Poesio & Vierra, 1998), their underlying production process has drawn many researchers’ attention, both from a computational and from a psycholinguistic perspective. Much of this research focusses on the question of what makes people choose one possible way of referring to an object over another. Why do speakers include certain properties in their descriptions and others not? Computational studies of reference often frame the production of referring expressions as a problem of choice where a (usually fixed) preference order determines the order in which attributes (such as color or orientation) are considered for inclusion in a referring expression. The Incremental Algorithm (Dale & Reiter, 1995), for instance, which is probably the most widely used algorithm in this field, operates according to this principle, assuming the existence of a domain-dependent preference order on the relevant attributes, and first tries out preferred attributes before less preferred ones are considered, thereby modeling the intuition that speakers prefer certain attributes over others, partly based on findings of Pechmann (1989). For example, when referring to a chair, speakers are more likely to refer to its color (the blue chair) than to its orientation (the chair facing left) even though both may be successful in singling out a particular chair. However, one could argue that the Incremental Algorithm is “addressee-blind” (Clark & Bangerter, 2004) in that it pays no attention to references that were produced earlier in an interaction (the same holds, incidentally, for the various alternatives that have been proposed to the Incremental Algorithm and which are surveyed in Krahmer and van Deemter, 2012). Indeed, it has been shown that speakers do take prior references into account during reference production. One study, for example, found that if one dialogue participant refers to a couch as a sofa, the next speaker is more likely to use the word sofa as well (Branigan, Pickering, Pearson, & McLean, 2010). This can be seen as a lexical form of “alignment” (Garrod & Pickering, 2004; Pickering & Garrod, 2004) between speaker and addressee. Pickering and Garrod argue that alignment may take place on all levels of interaction, and indeed it has been shown that participants also align, for example, their intonation patterns and syntactic structures. Goudbeek and Krahmer (2010, 2012) have shown that a similar kind of adaptation can take place at the conceptual level of attributes. While the Incremental Algorithm and its ilk only predict the use of dispreferred attributes when preferred attributes alone are unable to uniquely identify a given target object, Goudbeek and Krahmer showed that participants do use dispreferred attributes when these were used earlier in an interaction. In particular, they found that when one dialogue partner used a dispreferred attribute to refer to an object, the other dialogue partner was more likely to use values of that attribute in subsequent references as well. For example, in the furniture domain used in their experiments, participants could always uniquely identify an object by using its color (e.g., the green chair 1 ) or its orientation (the chair seen from the front). Of these two attributes, color is the preferred one, in the sense that Here and elsewhere we provide English translations of Dutch originals.