We examine the problem of generating definite noun phrases that are appropriate referring expressions; that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended (...) algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. (shrink)

Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feedforward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic (...) encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging. (shrink)

The article is the first Russian translation of the most well-known piece in conversation analysis, written by the founders of CA Harvey Sacks, Emanuel Schegloff and Gail Jefferson. It has become a milestone in the development of the discipline. The authors offer a comprehensive approach to the study of conversational interactions. The approach is based on the analysis of detailed transcripts of the records of natural conversations. The authors show that in the course of the conversation co-conversationalists use a number (...) of techniques to organize the turn-taking. These techniques are combined in four rules: the first option is the transfer of speakership via allocation of the next speaker by the current speaker; if this first option is not realized, turn-taking may happen via the self-selection by one of the participants; if the second option remains unrealized too, the current speaker continues speaking, with all three options being recurrently provided at all next transition relevant places. The result of the operation of these rules is an orderly conversation based on the principle “one speaker at a time.” According to the authors, this model is compatible with obvious observations concerning conversational practices that they make. The authors show that in every conversation there is a turn-taking system in operation, which provides for a flexible adaptation of the every conversation’s structure to any possible topics and any possible speakers’ identities. Such approach considers how the participants in social interactions order their communication with each other, achieving a sense of normally occurring interaction. (shrink)

This article offers a survey of computational research on referring expressions generation (REG). It introduces the REG problem and describes early work in this area, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years. We discuss computational frameworks underlying REG, and demonstrate a recent trend that seeks to link up REG algorithms with well-established Knowledge Representation traditions. Considerable attention is given to recent efforts at evaluating REG algorithms and the lessons that they (...) allow us to learn. The article concludes with a discussion of the way forward in REG, focussing on references in larger and more realistic settings. (shrink)

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference (...) production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression—as reflected by speech onset latencies—is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search. (shrink)

In two experiments, we investigate to what extent various visual saliency cues in realistic visual scenes cause speakers to overspecify their definite object descriptions with a redundant color attribute. The results of the first experiment demonstrate that speakers are more likely to redundantly mention color when visual clutter is present in a scene as compared to when this is not the case. In the second experiment, we found that distractor type and distractor color affect redundant color use: Speakers are most (...) likely to overspecify if there is at least one distractor object present that has the same type, but a different color than the target referent. Reliable effects of distractor distance were not found. Taken together, our results suggest that certain visual saliency cues guide speakers in determining which objects in a visual scene are relevant distractors, and which not. We argue that this is problematic for algorithms that aim to generate human-like descriptions of objects, since these generally select properties that help to distinguish a target from all objects that are present in a scene. (shrink)