LEXICAL ENTRAINMENT IN SPONTANEOUS DIALOG

It is often desirable to predict or constrain the lexical choices people make with spoken language systems. I discuss lexical variability in language use; while there is a great deal of variability across conversations, there is little within. In conversation, people achieve conceptual pacts, or shared conceptualizations, which they mark by using the same terms; this process of lexical entrainment limits and systematizes lexical variability (Brennan & Clark, 1996). In text and speech dialogs with computer systems, people often adopt the systems' terms; this process shares some aspects in common with lexical entrainment, and it differs in some ways as well. After summarizing five experiments on lexical variability with human partners and computers, I present some implications of this work for spoken language systems. 1. THE VOCABULARY PROBLEM There is potential for enormous variability in people's lexical choices in dialog, whether with human or machine partners. Not only is the mental lexicon large–by some estimates, a high school graduate may know as many as 100,000 words–but the same message can be expressed in an indefinite number of ways. This potential for variability has been dubbed the vocabulary problem by Furnas, Landauer, Gomez, and Dumais (1983, 1987) in their studies of command languages. Furnas et al. found that the probability of any two people producing the same term for the same command ranged from only .07 to .18 (Furnas et al., 1987). For instance, to remove a file, people might try such terms as remove, delete, erase, kill, omit, destroy, lose, change, and even trash. Command language designers have tried to cope with the vocabulary problem by having systems accept synonyms for commands (see, for instance, Good, Whiteside, Wixon, & Jones, 1984). But as Furnas et al. (1987) discovered in their experiments, even with as many as 20 synonyms for a single command, the likelihood that two people would choose the same term for that command was only about 80% (Furnas et al. 1987). Furthermore, synonyms lead to additional problems: In an application with only 25 commands, the likelihood that two people who chose the same term meant the same command by it was only 15%. For natural language interfaces without explicit constraints on the syntax of input utterances, there are many more choices. One group of computational linguists working on a natural language interface to a database query application listed 7000 distinct ways to ask about programmers who work for department managers, using common words and syntax (Brennan, 1990). And for speech interfaces, the vocabulary problem can be worse still. Speech interfaces are often designed to accept a limited vocabulary because both perplexity and processing time increase as the search space increases. Under these circumstances, what people can say at each point in the dialog is constrained. There are two common ways to do this: Some systems offer a small and explicit menu of choices, such as "yes" or "no" for people to accept or decline a collect call. Others place the burden on speakers to remember which terms are acceptable. The first of these approaches works only for highly scripted interactions under the system's control, while the second approach may work for expert users but not for casual ones. If our eventual goal is to provide people with spoken language interfaces that are both natural and robust, then we need a new approach. How do people solve the vocabulary problem when they talk with other people? To address this question, I will consider what we have learned about how people choose their terms while referring to objects in conversation. 2. LEXICAL ENTRAINMENT AND CONCEPTUAL PACTS IN CONVERSATION Lexical variability can be just as high in human conversation as in human-computer dialog. In one of our referential communication studies, the likelihood that people in one conversation would choose the same terms for the same common object as people in another conversation was only 10% (Brennan & Clark, 1996). But while variability is high between conversations, it is relatively low within a conversation (Brennan & Clark, 1996; Garrod & Anderson, 1987). When two people repeatedly discuss the same object, they come to use the same terms. This phenomenon, called lexical entrainment, has several possible explanations. An ahistorical explanation considers each choice of terms as an independent event, affected by informativeness, the availability of the terms, and the salience of the referent's features. The simplest ahistorical explanation is that speakers design their referring expressions to distinguish a referent from a set of alternatives (Brown, 1958; Olson, 1970) and that they choose the most concise expression that will enable their addressees to do so (Grice, 1975). So when speakers want to refer to an Irish Setter in a set that also contains a beagle and a poodle, they shouldn't use "dog," which isn't informative enough, or "the big red Irish Setter with the tongue hanging out," which is over-informative, but rather "the Irish Setter." In addition, availability (or ease of lexical access) plays a role; the most available labels are usually basic level nouns–"dog," for example, as opposed to "animal" or "Irish Setter" (Cruse, 1977; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). So when the set consists of the Irish Setter, a bicycle, and a hammer, then speakers should be likely to use "dog," a common basic level term (as opposed to "animal," which is informative enough). This account of lexical choice was presented by Cruse (1977), who proposed that the most common term that is informative enough is considered unmarked; all other terms are marked. An ahistorical, frequency-based explanation underlies the expectation that people in dialogs with computers will limit their choices to the most common lexical items (e.g., Guindon, 1991). But Furnas et al's. (1987) results suggest this expectation is unjustified. Historical explanations of lexical entrainment, on the other hand, appeal to past references within a conversation and to partner-specific conceptualizations that two people achieve interactively. We take the view that referring is a collaborative process (Clark & Wilkes-Gibbs, 1986). When a speaker labels an object, she is proposing a conceptualization of it, a proposal her addressee may or may not agree to. So her referring expression is provisional until accepted or modified by her partner. This view leads to several predictions: (1) once people establish a shared conceptualization or conceptual pact, they appeal to it in later references even when they could use simpler or less informative references; (2) speakers rely on conceptual pacts more often the more firmly they have been established, and (3) conceptual pacts are based not only on a speaker's own history, but on history with a particular addressee. In a series of experiments, we tested these predictions. Pairs of people who were visually separated conversed in order to line up identical sets of picture cards in the same order. The target cards (the items of interest to us) were a particular shoe, dog, car, and fish. In the A trials, the targets were all unique in their basic level categories, and so we expected that many people would choose basic level terms. In the B or nonunique trials, people matched a different set of cards that included the same four targets, but also additional shoes, dogs, cars, and fish; we expected that they would modify their labels to be more informative. In the C trials, they returned to matching the same set of (unique) cards as in the A trials. The trial of particular interest was the first C trial, in which speakers could return to using basic level terms (which were informative enough, as well as shorter than the more informative terms from the B trials). This is what an ahistorical view of lexical choice predicts. A historical view, on the other hand, predicts that in the first C trial, people should continue to use the same or similar terms as in the B trials (and not revert to basic level terms). This is what we found in two experiments; 46-52% of the time, people used the same terms in the first C trial as in the B trials, and when terms were not precisely the same, they were strongly related (our coding system used strict criteria for equivalence and counted "the big red dog with the tongue hanging out" as not the same as "the big red dog with the tongue"). On average there were 11 references to other objects between each reference to a particular target item (including some very complex references to abstract geometric objects that served as distracters) and so 46-52% represents a substantial degree of lexical entrainment. As for our second prediction, the likelihood of lexical entrainment in the first C trial was strongly affected by number of B trials (either one or four) that people had experienced; people were less likely to revert to basic level terms like "dog" and "fish," and more likely to rely on conceptual pacts, the more firmly these pacts had been established. In our third experiment, people matched four B trials (with the non-unique cards) and then did four C trials (with the same targets, but unique in their basic level categories) either with the same partner or with an entirely new partner (who had never matched the cards before). If people were really forming conceptual pacts with specific partners, they should use the same terms in the first C trial as in the last B trial more often when continuing to match cards with the same partner than when matching cards with a new partner. As predicted, the results showed a partner-specific effect; speakers with continuing addressees appealed to conceptual pacts they had already established, even when these were over-informative, while speakers with new addressees were more likely to switch to basic level terms in the first or subsequent C trials. Partner-specific effects have t