This response discusses the experiment reported in Krahmer et al.’s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ‘‘normal” (i.e., non-semantically-balanced) corpora.

The notion of common ground is important for the production of referring expressions: In order for a referring expression to be felicitous, it has to be based on shared information. But determining what information is shared and what information is privileged may require gathering information from multiple sources, and constantly coordinating and updating them, which might be computationally too intensive to affect the earliest moments of production. Previous work has found that speakers produce overinformative referring expressions, which include privileged names, (...) violating Grice’s Maxims, and concluded that this is because they do not mark the distinction between shared and privileged information. We demonstrate that speakers are in fact quite effective in marking this distinction in the form of their utterances. Nonetheless, under certain circumstances, speakers choose to overspecify privileged names. (shrink)

Algorithms for NLG NLG is typically broken down into stages of discourse planning (to select information and organize it into coherent paragraphs), sentence planning (to choose words and structures to fit information into sentence-sized units), and realization (to determine surface form of output, including word order, morphology and final formatting or intonation). The SPUD system combines the generation steps of sentence planning and surface realization by using a lexicalized grammar to construct the syntax and semantics of a sentence simultaneously.

This paper reports on an approach to model generalized implicatures using nonmonotonic logics. The approach, called compositional, is based on the idea of compositional semantics, where the implicatures carried by a sentence are constructed from the implicatures carried by its constituents, but it also includes some aspects nonmonotonic logics in order to model the defeasibility of generalized implicatures.

We combine state-of-the-art techniques from computational linguisticsand theorem proving to build an engine for playing text adventures,computer games with which the player interacts purely through naturallanguage. The system employs a parser for dependency grammar and ageneration system based on TAG, and has components for resolving andgenerating referring expressions. Most of these modules make heavy useof inferences offered by a modern theorem prover for descriptionlogic. Our game engine solves some problems inherent in classical textadventures, and is an interesting test case for (...) the interactionbetween natural language processing and inference. (shrink)

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this (...) paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

Two experiments examined the restriction of referential domains during unscripted conversation by analyzing the modification and online interpretation of referring expressions. Experiment 1 demonstrated that from the earliest moments of processing, addressees interpreted referring expressions with respect to referential domains constrained by the conversation. Analysis of eye movements during the conversation showed elimination of standard competition effects seen with scripted language. Results from Experiment 2 pinpointed two pragmatic factors responsible for restriction of the referential domains used by speakers to design (...) referential expressions and demonstrated that the same factors predict whether addressees consider local competitors to be potential referents during online interpretation of the same expressions. These experiments demonstrate, for the first time, that online interpretation of referring expressions in conversation is facilitated by referential domains constrained by pragmatic factors that predict when addressees are likely to encounter temporary ambiguity in language processing. (shrink)

This article introduces the topic ‘‘Production of Referring Expressions: Bridging the Gap between Computational and Empirical Approaches to Reference’’ of the journal Topics in Cognitive Science. We argue that computational and psycholinguistic approaches to reference production can benefit from closer interaction, and that this is likely to result in the construction of algorithms that differ markedly from the ones currently known in the computational literature. We focus particularly on determinism, the feature of existing algorithms that is perhaps most clearly at (...) odds with psycholinguistic results, discussing how future algorithms might include non-determinism, and how new psycholinguistic experiments could inform the development of such algorithms. (shrink)

We translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to exploit the performance of off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action.

Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking may be relatively effortful. Yet adults routinely engage in effortful processes when needed. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that the shared goal of communication induces a natural division of labor: The resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's (...) allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal trade-off between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director–matcher task. We found that speakers spontaneously modulated the informativeness of their descriptions to account for “known unknowns” in their partner's private view, reflecting a higher degree of speaker perspective-taking than previously acknowledged. In Experiment 2, we then compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply “mindblind”; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource-rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...) the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

Experiments on visually grounded, definite reference production often manipulate simple visual scenes in the form of grids filled with objects, for example, to test how speakers are affected by the number of objects that are visible. Regarding the latter, it was found that speech onset times increase along with domain size, at least when speakers refer to nonsalient target objects that do not pop out of the visual domain. This finding suggests that even in the case of many distractors, speakers (...) perform object‐by‐object scans of the visual scene. The current study investigates whether this systematic processing strategy can be explained by the simplified nature of the scenes that were used, and if different strategies can be identified for photo‐realistic visual scenes. In doing so, we conducted a preregistered experiment that manipulated domain size and saturation; replicated the measures of speech onset times; and recorded eye movements to measure speakers’ viewing strategies more directly. Using controlled photo‐realistic scenes, we find (1) that speech onset times increase linearly as more distractors are present; (2) that larger domains elicit relatively fewer fixation switches back and forth between the target and its distractors, mainly before speech onset; and (3) that speakers fixate the target relatively less often in larger domains, mainly after speech onset. We conclude that careful object‐by‐object scans remain the dominant strategy in our photo‐realistic scenes, to a limited extent combined with low‐level saliency mechanisms. A relevant direction for future research would be to employ less controlled photo‐realistic stimuli that do allow for interpretation based on context. (shrink)

This article explores the role of surface ambiguities in referring expressions, and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions, if these expressions are to be optimally effective for a hearer. We focus on the ambiguities that arise when adjectives occur in coordinated structures. The central idea is to use statistical information about lexical co‐occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid (...) generating phrases where misunderstandings are likely. Various aspects of the problem were explored in three experiments in which responses by human participants provided evidence about which reading was most likely for certain phrases, which phrases were deemed most suitable for particular referents, and the speed at which various phrases were read. We found a preference for ‘‘clear’’ expressions to ‘‘unclear’’ ones, but if several of the expressions are ‘‘clear,’’ then brief expressions are preferred over non‐brief ones even though the brief ones are syntactically ambiguous and the non‐brief ones are not; the notion of clarity was made precise using Kilgarriff's Word Sketches. We outline an implemented algorithm that generates noun phrases conforming to our hypotheses. (shrink)

A fundamental part of the process of referring to an entity is to categorise it (for instance, as the woman). Where multiple categorisations exist, this implicitly involves the adoption of a conceptual perspective. A challenge for the automatic Generation of Referring Expressions is to identify a set of referents coherently, adopting the same conceptual perspective. We describe and evaluate an algorithm to achieve this. The design of the algorithm is motivated by the results of psycholinguistic experiments.

Paulina Sliwa (2015) argues that knowing why p is necessary and sufficient for understanding why p. She tries to rebut recent attacks against the necessity and sufficiency claims, and explains the gradability of understanding why in terms of knowledge. I argue that her attempts do not succeed, but I indicate more promising ways to defend reductionism about understanding why throughout the discussion.

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)

Children often refer to things ambiguously but learn not to from responding to clarification requests. We review and explore this learning process here. In Study 1, eighty-four 2- and 4-year-olds were tested for their ability to request stickers from either (a) a small array with one dissimilar distracter or (b) a large array containing similar distracters. When children made ambiguous requests, they received either general feedback or specific questions about which of two options they wanted. With training, children learned to (...) produce more complex object descriptions and did so faster in the specific feedback condition. They also tended to provide more information when requesting stickers from large arrays. In Study 2, we varied only distracter similarity during training and then varied array size in a generalization test. Children found it harder to learn in this case. In the generalization test, 4-year-olds were more likely to provide information (a) when it was needed because distracters were similar to the target and (b) when the array size was greater (regardless of need for information). We discuss how clear cues to potential ambiguity are needed for children to learn to tailor their referring expression to context and how several cues of heuristic value (e.g., more distracters > say more) can promote the efficiency of communication while language is developing. Finally, we consider whether it would be worthwhile drawing on the human learning process when developing algorithms for the production of referring expressions. (shrink)

This article explores the role of surface ambiguities in referring expressions, and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions, if these expressions are to be optimally effective for a hearer. We focus on the ambiguities that arise when adjectives occur in coordinated structures. The central idea is to use statistical information about lexical co-occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid (...) generating phrases where misunderstandings are likely. Various aspects of the problem were explored in three experiments in which responses by human participants provided evidence about which reading was most likely for certain phrases, which phrases were deemed most suitable for particular referents, and the speed at which various phrases were read. We found a preference for ‘‘clear’’ expressions to ‘‘unclear’’ ones, but if several of the expressions are ‘‘clear,’’ then brief expressions are preferred over non-brief ones even though the brief ones are syntactically ambiguous and the non-brief ones are not; the notion of clarity was made precise using Kilgarriff's Word Sketches. We outline an implemented algorithm that generates noun phrases conforming to our hypotheses. (shrink)

In this paper, we introduce a system, Sentence Planning Using Description, which generates collocations within the paradigm of sentence planning. SPUD simultaneously constructs the semantics and syntax of a sentence using a Lexicalized Tree Adjoining Grammar (LTAG). This approach captures naturally and elegantly the interaction between pragmatic and syntactic constraints on descriptions in a sentence, and the inferential and lexical interactions between multiple descriptions in a sentence. At the same time, it exploits linguistically motivated, declarative speci- fications of the discourse (...) functions of syntactic constructions to make contextually appropriate syntactic choices. (shrink)

Speakers’ perception of a visual scene influences the language they use to describe it—which objects they choose to mention and how they characterize the relationships between them. We show that visual complexity can either delay or facilitate description generation, depending on how much disambiguating information is required and how useful the scene's complexity can be in providing, for example, helpful landmarks. To do so, we measure speech onset times, eye gaze, and utterance content in a reference production experiment in which (...) the target object is either unique or non-unique in a visual scene of varying size and complexity. Speakers delay speech onset if the target object is non-unique and requires disambiguation, and we argue that this reflects the cost of deciding on a high-level strategy for describing it. The eye-tracking data demonstrate that these delays increase when speakers are able to conduct an extensive early visual search, implying that when speakers scan too little of the scene early on, they may decide to begin speaking before becoming aware that their description is underspecified. Speakers’ content choices reflect the visual makeup of the scene—the number of distractors present and the availability of useful landmarks. Our results highlight the complex role of visual perception in reference production, showing that speakers can make good use of complexity in ways that reflect their visual processing of the scene. (shrink)

In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a good one. (...) In this brief note, however, we suggest (based on a learning curve experiment) that finding a Preference Order for a new domain may not be so difficult after all, as long as one has access to a handful of human-produced descriptions collected in a semantically transparent way. We argue that this is due to the fact that it is both more important and less difficult to get a good ordering of the head than of the tail of a Preference Order. (shrink)

Psycholinguistic studies often look at the production of referring expressions in interactive settings, but so far few referring expression generation algorithms have been developed that are sensitive to earlier references in an interaction. Rather, such algorithms tend to rely on domain-dependent preferences for both content selection and linguistic realization. We present three experiments showing that humans may opt for dispreferred attributes and dispreferred modifier orderings when these were primed in a preceding interaction (without speakers being consciously aware of this). In (...) addition, we show that speakers are more likely to produce overspecified references, including dispreferred attributes (although minimal descriptions with preferred attributes would suffice), when these were similarly primed. (shrink)

We relate the theory of presupposition accommodation to a computational framework for reasoning in conversation. We understand presuppositions as private commitments the speaker makes in using an utterance but expects the listener to recognize based on mutual information. On this understanding, the conversation can move forward not just through the positive effects of interlocutors’ utterances but also from the retrospective insight interlocutors gain about one anothers’ mental states from observing what they do. Our title, ENLIGHTENED UPDATE, highlights such cases. Our (...) approach fleshes out two key principles: that interpretation is a form of intention recognition; and that intentions are complex informational structures, which specify commitments to conditions and to outcomes as well as to actions. We present a formalization and implementation of these principles for a simple conversational agent, and draw on this case study to argue that pragmatic reasoning is holistic in character, continuous with common-sense reasoning about collaborative activities, and most effectively characterized by associating specific, reliable interpretive constraints directly with grammatical forms. In showing how to make such claims precise and to develop theories that respect them, we illustrate the general place of computation in the cognitive science of language. (shrink)

We report two experiments that investigated the widely held assumption that speakers use the addressee’s discourse model when choosing referring expressions (e.g., Ariel, 1990; Chafe, 1994; Givón, 1983; Prince, 1985), by manipulating whether the addressee could hear the immediately preceding linguistic context. Experiment 1 showed that speakers increased pronoun use (and decreased noun phrase use) when the referent was mentioned in the immediately preceding sentence compared to when it was not, even though the addressee did not hear the preceding sentence, (...) indicating that speakers used their own, privileged discourse model when choosing referring expressions. The same pattern of results was found in Experiment 2. Speakers produced more pronouns when the immediately preceding sentence mentioned the referent than when it mentioned a referential competitor, regardless of whether the sentence was shared with their addressee. Thus, we conclude that choice of referring expression is determined by the referent’s accessibility in the speaker’s own discourse model rather than the addressee’s. (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)

The following list contains a survey of some important and recent research in modeling face-to-face conversation. The list below is a presented as a guide to the literature by topic and date; we include complete citations afterwards in alphabetical order. For brevity, research works are keyed by first author and date only (we use these keys on the slides as well as in this list). Of course, most papers are multiply authored. The list is not intended to be exhaustive. Our (...) primary aim is simply to provide bibliographic information for all the research that we will refer to during the ESSLLI class itself. The entries also provide a sampling from ongoing research projects so that you can get an overall sense of the state of the field and begin to follow up topics of particular interest to you. (shrink)

This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task—a task-oriented dialog. One general model is based on Dale and Reiter’s (1995)incremental algorithm, and the other is a simple template model that has a higher correlation with the data but is specifically geared toward the properties of the iMAP task. The property of the iMAP task environment that is modeled here is that the color feature is unreliable for identifying referents while other features (...) are reliable. The low computational cost of the incremental algorithm for generating referring expressions makes it an interesting starting point for a cognitive model. However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order. The first model extends the original incremental algorithm by an ability to adapt to feedback of whether a referring expression was used successfully, but it seems to overpredict the frequency with which distinguishing expressions are made and underpredict the frequency of overspecified referring expressions. The second model produces features for referring expressions purely based on its current estimate of a feature’s utility. Both models predict the observed human behavior of decreasing use of color terms and increasing use of useful feature terms. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one‐shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...) the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

When referring to a target object in a visual scene, speakers are assumed to consider certain distractor objects to be more relevant than others. The current research predicts that the way in which speakers come to a set of relevant distractors depends on how they perceive the distance between the objects in the scene. It reports on the results of two language production experiments, in which participants referred to target objects in photo-realistic visual scenes. Experiment 1 manipulated three factors that (...) were expected to affect perceived distractor distance: two manipulations of perceptual grouping (region of space and type similarity), and one of presentation mode (2D vs. 3D). In line with most previous research on visually-grounded reference production, an offline measure of visual attention was taken here: the occurrence of overspecification with color. The results showed effects of region of space and type similarity on overspecification, suggesting that distractors that are perceived as being in the same group as the target are more often considered relevant distractors than distractors in a different group. Experiment 2 verified this suggestion with a direct measure of visual attention, eye tracking, and added a third manipulation of grouping: color similarity. For region of space in particular, the eye movements data indeed showed patterns in the expected direction: distractors within the same region as the target were fixated more often, and longer, than distractors in a different region. Color similarity was found to affect overspecification with color, but not gaze duration or the number of distractor fixations. Also the expected effects of presentation mode (2D vs. 3D) were not convincingly borne out by the data. Taken together, these results provide direct evidence for the close link between scene perception and language production, and indicate that perceptual grouping principles can guide speakers in determining the distractor set during reference production. (shrink)

It has often been observed that color is a highly preferred attribute for use in distinguishing descriptions, that is, referring expressions produced with the purpose of identifying an object within a visual scene. However, most of these observations were based on visual displays containing only colors that were maximally different in hue and for which the language of experimentation possessed basic color terms. The experiments described in this paper investigate whether speakers’ preference for color is reduced if the color of (...) the target referent is similar to that of the distractors. Because colors that look similar are often also harder to distinguish linguistically, we also examine the impact of the codability of color values. As a third factor, we investigate the salience of available alternative attributes and its impact on the use of color. The results of our experiments show that, while speakers are indeed less likely to use color when the colors in a display are similar, this effect is mostly due to the difficulty in naming similar colors. Color use for color with a basic color term is affected only when the colors of target and distractors are very similar (yet still distinguishable). The salience of our alternative attribute size, manipulated by varying the difference in size between target and distractors, had no impact on the use of color. (shrink)

This study investigates to what extent the amount of variation in a visual scene causes speakers to mention the attribute color in their definite target descriptions, focusing on scenes in which this attribute is not needed for identification of the target. The results of our three experiments show that speakers are more likely to redundantly include a color attribute when the scene variation is high as compared with when this variation is low (even if this leads to overspecified descriptions). We (...) argue that these findings are problematic for existing algorithms that aim to automatically generate psychologically realistic target descriptions, such as the Incremental Algorithm, as these algorithms make use of a fixed preference order per domain and do not take visual scene variation into account. (shrink)

It has often been observed that color is a highly preferred attribute for use in distinguishing descriptions, that is, referring expressions produced with the purpose of identifying an object within a visual scene. However, most of these observations were based on visual displays containing only colors that were maximally different in hue and for which the language of experimentation possessed basic color terms. The experiments described in this paper investigate whether speakers’ preference for color is reduced if the color of (...) the target referent is similar to that of the distractors. Because colors that look similar are often also harder to distinguish linguistically, we also examine the impact of the codability of color values. As a third factor, we investigate the salience of available alternative attributes and its impact on the use of color. The results of our experiments show that, while speakers are indeed less likely to use color when the colors in a display are similar, this effect is mostly due to the difficulty in naming similar colors. Color use for color with a basic color term is affected only when the colors of target and distractors are very similar. The salience of our alternative attribute size, manipulated by varying the difference in size between target and distractors, had no impact on the use of color. (shrink)

I show how a conversational process that takes simple, intuitively meaningful steps may be understood as a sophisticated computation that derives the richly detailed, complex representations implicit in our knowledge of language. To develop the account, I argue that natural language is structured in a way that lets us formalize grammatical knowledge precisely in terms of rich primitives of interpretation. Primitives of interpretation can be correctly viewed intentionally, as explanations of our choices of linguistic actions; the model therefore fits our (...) intuitions about meaning in conversation. Nevertheless, interpretations for complex utterances can be built from these primitives by simple operations of grammatical derivation. In bridging analyses of meaning at semantic and symbol‐processing levels, this account underscores the fundamental place for computation in the cognitive science of language use. (shrink)