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

A hearer-oriented evaluation of referring expression generation

This work is supported by a University of Aberdeen Sixth Century Studentship, and EPSRC grant EP/E011764/1.This paper discusses the evaluation of a Generation of Referring Expressions algorithm that takes structural ambiguity into account. We describe an ongoing study with human readers.peer-reviewe

Generating Effective Instructions: Knowing When to Stop

One aspect of Natural Language generation is describing entities so that they are distinguished from all other entities. Entities include objects, events, actions, and states. Much attention has been paid to objects and the generation of their referring expressions (descriptions meant to pick out or refer to an entity). However, a growing area of research is the automated generation of instruction manuals and an important part of generating instructions is distinguishing the actions that are to be carried out from other possible actions. One distinguishing feature is an action\u27s termination, or when the performance of the action is to stop. My dissertation work focuses on generating action descriptions from action information using the SPUD generation algorithm developed here at Penn by Matthew Stone. In my work, I concentrate on the generation of expressions of termination information as part of action descriptions. The problems I address include how termination information is represented in action information and expressed in Natural Language, how to determine when an action description allows the reader to understand how to perform the action correctly, and how to generate the appropriate description of action information

Varieties of specification: Redefining over-and under-specification

A long tradition of research in theoretical, experimental and computational pragmatics has investigated over-specification and under-specification in referring expressions. Along broadly Gricean lines, these studies compare the amount of information expressed by a referring expression against the amount of information that is required. Often, however, these studies offer no formal definition of what “required” means, and how the comparison should be performed. In this paper, we use a simple set-theoretic perspective to define some communicatively important types of over-/under-specification. We argue that our perspective enables an enhanced understanding of reference phenomena that can pay important dividends for the analysis of reference in corpora and for the evaluation of computational models of referring. To illustrate and substantiate our claims, we analyse two corpora, containing Chinese and English referring expressions respectively, using the new perspective. The results show that interesting new monolingual and cross-linguistic insights can be obtained from our perspective

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

Generating Anaphoric Expressions : Contextual Reasoning in Sentence Planning

This thesis investigates the contextual reasoning involved in the production of anaphoric expressions in natural language generation systems. More specifically, I propose generation strategies for two types of discourse anaphora which have not been treated in generation before: bridging descriptions and additive particles. To this end the contextual conditions that govern the use of these expressions have to be formalized. The formalization that I propose is based on notions from linguistics and extends previous approaches to the generation of co-referential anaphora. I then specify the reasoning tasks that have to be carried out in order to check the contextual conditions. I describe how they can be implemented using a state-of-the-art reasoning system for description logics, and I compare my proposal to alternative approaches using other kinds of reasoning tools. Finally, I describe an experimental implementation of the proposed approach.Diese Arbeit untersucht die kontextuellen Schlussfolgerungen, die bei der Erzeugung von anaphorischen Ausdrücken in Textgenerierungssystemen anfallen. Ich entwickele Generierungsstratgien für zwei Arten von Diskursanaphern, die bisher nicht in der Generierung behandelt worden sind: indirekte Anaphern und additive Partikel. Dafür müssen die kontextuellen Bedingungen, unter denen diese Ausdrücke verwendet werden, bestimmt und formalisiert werden. Die Formalisierung, die ich vorschlage, basiert auf linguistischen Theorien und verallgemeinert ältere Ansätze zur Generierung von koreferentiellen Anaphern. Dann spezifiziere ich die Inferenzaufgaben, die gelöst werden müssen, um die kontextuellen Bedingungen zu überprüfen. Ich beschreibe, wie sie mit Hilfe von aktuellen Inferenzsystemen für terminologische Logiken implementiert werden können, und vergleiche meinen Ansatz mit Alternativen, die andere Arten von logischen Inferenzsystemen nutzen. Schließlich, beschreibe ich eine experimentelle Implementation meines Ansatzes