37,431 research outputs found
Textual Economy through Close Coupling of Syntax and Semantics
We focus on the production of efficient descriptions of objects, actions and
events. We define a type of efficiency, textual economy, that exploits the
hearer's recognition of inferential links to material elsewhere within a
sentence. Textual economy leads to efficient descriptions because the material
that supports such inferences has been included to satisfy independent
communicative goals, and is therefore overloaded in Pollack's sense. We argue
that achieving textual economy imposes strong requirements on the
representation and reasoning used in generating sentences. The representation
must support the generator's simultaneous consideration of syntax and
semantics. Reasoning must enable the generator to assess quickly and reliably
at any stage how the hearer will interpret the current sentence, with its
(incomplete) syntax and semantics. We show that these representational and
reasoning requirements are met in the SPUD system for sentence planning and
realization.Comment: 10 pages, uses QobiTree.te
Cost-based attribute selection for GRE (GRAPH-SC/GRAPH-FP)
In this paper we discuss several approaches to the problem of content determination for the generation of referring expressions (GRE) using the Graphbased framework of Krahmer et al. (2003). This work was carried out in the context of the First NLG Shared Task and Evaluation Challenge on Attribute Selection for Referring Expression Generation
Controlling redundancy in referring expressions
Krahmer et al.ās (2003) graph-based framework provides an elegant and flexible approach to the generation of referring expressions. In this paper, we present the first reported study that systematically investigates how to tune the parameters of the graph-based framework on the basis of a corpus of human-generated descriptions. We focus in particular on replicating the redundant nature of human referring expressions, whereby properties not strictly necessary for identifying a referent are nonetheless included in descriptions. We show how statistics derived from the corpus data can be integrated to boost the frameworkās performance over a non-stochastic baseline
Salience and pointing in multimodal reference
Pointing combined with verbal referring is one of the most paradigmatic human multimodal behaviours. The aim of this paper is foundational: to uncover the central notions that are required for a computational model of human-generated multimodal referring acts. The paper draws on existing work on the generation of referring expressions and shows that in order to extend that work with pointing, the notion of salience needs to play a pivotal role. The paper investigates the role of salience in the generation of referring expressions and introduces a distinction between two opposing approaches: salience-first and salience-last accounts. The paper then argues that these differ not only in computational efficiency, as has been pointed out previously, but also lead to incompatible empirical predictions. The second half of the paper shows how a salience first account nicely meshes with a range of existing empirical findings on multimodal reference. A novel account of the circumstances under which speakers choose to point is proposed that directly links salience with pointing. Finally, a multidimensional model of salience is proposed to flesh this model out
The automatic generation of narratives
We present the Narrator, a Natural Language Generation component used in a digital storytelling system. The system takes as input a formal representation of a story plot, in the form of a causal network relating the actions of the characters to their motives and their consequences. Based on this input, the Narrator generates a narrative in Dutch, by carrying out tasks such as constructing a Document Plan, performing aggregation and ellipsis and the generation of appropriate referring expressions. We describe how these tasks are performed and illustrate the process with examples, showing how this results in the generation
of coherent and well-formed narrative texts
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Factors causing overspecification in definite descriptions
Speakers often overspecify their target descriptions and include more information than necessary for unique identification of the target referent. In the current paper, we study the production of definite target descriptions, and explore several factors that might influence the amount of information that is included in these descriptions. First, we present the results of a large-scale experiment investigating referential overspecification as a function of the properties of a target referent and the communicative setting. The results show that speakers (both in written and oral conditions) tend to provide more information when a target is plural rather than singular, and in domains where the speaker has more referential possibilities to describe the target. However, written and spoken referring expressions do not differ in terms of semantic redundancy. We conclude our paper by discussing the implications of our empirical findings for pragmatic theory and for language production models.peer-reviewe
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