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)

In this paper we report on an investigation into the principles underlying the choice of a particular referential expression to refer to an object located in a domain to which both participants in the dialogue have visual as well as physical access. Our approach is based on the assumption that participants try to use as little effort as possible when referring to objects. This assumption is operational-ized in two factors, namely the focus of attention and a particular choice of features (...) to be included in a referential expression. We claim that both factors help in reducing the effort needed to, on the one hand, refer to an object and, on the other hand, to identify it. As a result of the focus of attention the number of potential target objects is reduced. The choice of a specific type of feature determines the number of objects that have to be identified in order to be able to understand the referential expression. An empirical study was conducted in which pairs of participants cooperatively carried out a simple block-building task, and the results provided empirical evidence that supported the aforementioned claims. Especially the focus of attention turned out to play an important role in reducing the total effort. Additionally, focus acted as a strong coherence-establishing device in the studied domain. (shrink)

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...

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)

Most everyday tasks involve multiple modalities, which raises the question of how the processing of these modalities is coordinated by the cognitive system. In this paper, we focus on the coordination of visual attention and linguistic processing during speaking. Previous research has shown that objects in a visual scene are fixated before they are mentioned, leading us to hypothesize that the scan pattern of a participant can be used to predict what he or she will say. We test this hypothesis (...) using a data set of cued scene descriptions of photo-realistic scenes. We demonstrate that similar scan patterns are correlated with similar sentences, within and between visual scenes; and that this correlation holds for three phases of the language production process (target identification, sentence planning, and speaking). We also present a simple algorithm that uses scan patterns to accurately predict associated sentences by utilizing similarity-based retrieval. (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)

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)

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)

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)