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

The Role of Graduality for Referring Expression Generation in Visual Scenes

International audienceReferring Expression Generation (reg) algorithms, a core component of systems that generate text from non-linguistic data, seek to identify domain objects using natural language descriptions. While reg has often been applied to visual domains, very few approaches deal with the problem of fuzziness and gradation. This paper discusses these problems and how they can be accommodated to achieve a more realistic view of the task of referring to objects in visual scenes

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

The role of graduality for referring expression generation in visual scenes

Referring Expression Generation (reg) algorithms, a core component of systems that generate text from non-linguistic data, seek to identify domain objects using natural language descriptions. While reg has often been applied to visual domains, very few approaches deal with the problem of fuzziness and gradation. This paper discusses these problems and how they can be accommodated to achieve a more realistic view of the task of referring to objects in visual scenes.peer-reviewe

Need I say more? On factors causing referential overspecification

We present the results of an elicitation experiment conducted to investigate which factors cause speakers to overspecify their referential expressions, where we hypothesized properties of the target and properties of the communicative setting to play a role. The results of this experiment show that speakers tend to provide more information when referring to a target in a more complex domain and when referring to plural targets. Moreover, written and spoken referring expressions do not differ in terms of redundancy, but do differ in terms of the number of words that they contain: speakers need more words to provide the same information as people who type their expressions.peer-reviewe

(NON)-DETERMINING THE ORIGINAL SPEAKER: REPORTATIVE PARTICLES VERSUS VERBS

This work argues that the Basque reportative particle omen contributes to the propositional contents of the utterance, and it is not an illocutionary force indicator, contrary to what seems to be suggested by the standard view on omen. The results of the application of the assent/dissent test for the case of omen show that subjects not only accept a rejection of the reported content (p), but also a rejection of the evidential content (pomen) itself. The results are similar to those of the verb esan ‘to say’. It is, then, proposed that the difference between these two elements can be explained by distinguishing between the contents of the utterances (with Korta & Perry 2007, 2011), regarding the (non-)articulation of the original speaker