Analyzing Speaker Strategy in Referential Communication (2020)

Abstract: We analyze a corpus of referential communication through the lens of quantitative models of speaker reasoning. Different models place different emphases on linguistic reasoning and collaborative reasoning. This leads models to make different assessments of the risks and rewards of using specific utterances in specific contexts. By fitting a latent variable model to the corpus, we can exhibit utterances that give systematic evidence of the diverse kinds of reasoning speakers employ, and build integrated models that recognize not only speaker reference but also speaker reasoning.

## introduction language users are able to work together to identify objects in the world @xcite . this ability involves formulating creative utterances, assessing their meaning in context, and anticipating listeners' understanding and response @xcite . despite long study, fundamental questions remain unanswered about how people manage this complex problem solving. this paper explores one question in particular: how speakers establish that references are likely to be successful. in general, such expectations can be underwritten either linguistically, by reasoning about the meanings and denotations of candidate linguistic expressions, or cooperatively, by reasoning about and anticipating their interlocutors' collaborative problem solving. both kinds of reasoning are undoubtedly common, and both play a significant role in the psychological and computational literature on referential communication. in this paper, we use quantitative cognitive models, fit to naturalistic corpora, to characterize the contributions of linguistic and cooperative reasoning in the spontaneous strategies of human interlocutors in referential communication. our re-search offers a number of contributions for the sigdial community. our findings give new detail to the received understanding of collaborative problem solving in dialogue. interlocutors often improvise, using risky strategies, in problematic situations; in these cases, they may have to work together interactively to achieve mutual understanding. we believe that the researchers working on computational discourse and dialogue are uniquely positioned to take up these results to build more powerful models of the reasoning of human speakers, and to use analogous models in the choices of automated systems. at the same time, we argue that appreciating the diversity of dialogue is necessary to build interactive systems that understand and respond appropriately to their human users. our work culminates in a mixture model that, given a description, predicts not only what the likely referent is, but also what reasoning the speaker was likely to have used to produce it. ## linguistic and cooperative reasoning our work is motivated by a distinction between reasoning linguistically, about meanings and denotations, and reasoning cooperatively, about understanding and collaboration. we begin by reviewing the theoretical and practical literature behind the two different approaches. to be clear, many reference problems have simple, good solutions that any reasoning will find. differences arise in more complicated cases, when speakers need to exploit the flexibility of linguistic meaning or the ability of the listener to recognize implicatures, and when speakers need to trade off between specific and general referring expressions. for clarity, our discussion illustrates these effects with concrete examples, even though this requires us to anticipate some results from later in the paper. in particular, we draw on attested examples from the colors in context (cic) dataset of @xcite , where a director must signal one target in a display of three color patches. an example is shown in figure 1 . we characterize the examples in terms of the quantitative predictions of models (described in full detail in section 3), which formalize linguistic reasoning and cooperative reasoning. these modx 0 x 1 x 2 director: the very dull red matcher: lighter or darker director: lighter figure 1 : an example from the colors in context (cic) dataset @xcite of the director and matcher coordinating so that the matcher can click on the correct color patch (x 0 ). els adopt a decision-theoretic approach. utterances achieve various outcomes with different probabilities. for example, we may be uncertain whether an utterance will be judged appropriate to the context, whether it will be understood correctly-and so whether it will be successful in advancing referential communication. safer utterances, with a higher probability of success, contrast with riskier utterances, with lower probability of success. in tandem, each utterance has a cost (fixed across models), which determines the utility obtained when the utterance is successful. a rare utterance, like chartreuse, is modeled as having a higher cost than a more frequent utterance with the same meaning, like yellow-green. in fact, general terms, like blue-gray, which describe a comparatively large subset of color space, are typically assigned a lower cost than more specific terms, like slate, which describe a narrower subset. this is because, in situations where general terms and specific terms both offer equal prospects of task success, human speakers tend to prefer the general ones. this preference is particularly strong for basic-level terms @xcite , like blue. overall then, the models assign each utterance an expected utility, which combines risk and cost in a single preference ranking. as is common in empirical models of human choice @xcite , speakers are modeled as stochastic, approximate utility maximizers. the greater the utility advantage of the best choice, the more likely speakers are to use it; less advantageous choices are unlikely but not impossible. this assumption translates the model of expected utility into a distribution over potential descriptions (w) conditioned on the target color patch (x 0 ) and context of all three color patches (c). ## learning speaker reasoning we formulate computational models of speaker reasoning in two steps. first, as described in section 3.1, we build the xkcd model based on the applicability and cost of color terms, following @xcite ; @xcite ; @xcite . second, we describe the linguistic and cooperative reasoning choices of speakers as a function of these learned parameters. as described in section 3.2, our models of linguistic reasoning use probabilistic models of vagueness to formulate low-cost descriptions that denote the target uniquely @xcite . meanwhile, as described in section 3.3, our models of cooperative reasoning use probabilistic planning to find low-cost utterances likely to be understood by the listener, following the rational speech acts approach (frank and goodman, 2012). ## discussion and conclusion this paper has argued that human speakers in collaborative reference dialogues take diverse strategies: they can stick with clear, precise descriptions; alternatively, they can create innovative interpretations for words; alternatively, they can count on their audience to fill in the gaps in what they say. while computational models often focus on one specific kind of reasoning, we believe that our findings are broadly consonant with the psycholinguistics literature, with its evidence of the psychological difficulty of semantically identifying targets @xcite , its evidence of the psychological difficulty of taking the audience's perspective into account @xcite , and its concepts of "least collaborative effort" @xcite in characterizing interaction as fundamental to success in conversation. we are optimistic that future work can continue to develop precise data-driven models that integrate these different explanations to understand and respond to user utterances in dialogue systems. our work has a number of limitations that we leave for future research. even within the simple domain of identifying color patches, we see the utterances that rgc cannot explain-utterances where a speaker seems to refer to a target object with a description that fits the target less well than a distractor-as a strong indication of variability in meaning across individuals. this needs to be accounted for. in addition, it would be good to explore models of reference to colors in context that generalize from colors in isolation data using more flexible machine-learned models of choice. what about more complex domains and interactions? the challenges of providing fine-grained and wide-ranging analyses of interlocutors' referential problem-solving strategies remain substantial. neverthless, we do see promising directions. one is to follow @xcite in conceptualizing reference production in terms of a high-level choice of strategy followed by detailed content choices, and build a corresponding probabilistic model of reference production. another is cover more complex interactions, by including additional interactive strategies for framing alternatives, excluding wrong interpretations, asking clarification questions, and answering them.