Modality Choice for Generation of Referring Acts: Pointing versus Describing

The main aim of this paper is to challenge two commonly held assumptions regarding modality selection in the generation of referring acts: the assumption that non-verbal means of referring are secondary to verbal ones, and the assumption that there is a single strategy that speakers follow for generating referring acts. Our evidence is drawn from a corpus of task-oriented dialogues that was obtained through an observational study. We propose two alternative strategies for modality selection based on correlation data from the observational study. Speakers that follow the first strategy simply abstain from pointing. Speakers that follow the other strategy make the decision whether to point dependent on whether the intended referent is in focus and/or important. This decision precedes the selection of verbal means (i.e., words) for referring

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

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