Towards automatic generation of natural language generation systems (original) (raw)
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Multilingual sentence generation
Proceedings of the 8th European workshop on Natural Language Generation - EWNLG '01, 2001
This paper presents an overview of a robust, broad-coverage, and application-independent natural language generation system. It demonstrates how the different language generation components function within a multilingual Machine Translation (MT) system, using the languages that we are currently working on (English, Spanish, Japanese, and Chinese). Section 1 provides a system description. Section 2 focuses on the generation components and their core set of rules. Section 3 describes an additional layer of generation rules included to address applicationspecific issues. Section 4 provides a brief description of the evaluation method and results for the MT system of which our generation components are a part.
Handbook of Natural Language Processing, 2000
We report here on a significant new set of capabilities that we have incorporated into our language generation system MUMBLE. Their impact will be to greatly simplify the work of any text planner that uses MUMBLE as ita linguistics component since MUMBLE can now take on many of the planner's text organization and decision-making problems with markedly less hand-tailoring of algorithms in either component.
Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009)}
Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009)}, 2009
@Book{ENLG:2009, editor = {Emiel Krahmer and Mari\"{e}t Theune}, title = {Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009)}, month = {March}, year = {2009}, address = {Athens, Greece}, publisher = {Association for Computational Linguistics}, url = {http://www.aclweb.org/anthology/W09-06} } @InProceedings{reiter-EtAl:2009:ENLG, author = {Reiter, Ehud and Turner, Ross and Alm, Norman and Black, Rolf and Dempster, Martin and Waller, Annalu}, title = {Using {NLG} to Help Language-Impaired Users Tell ...
Symbolic Authoring for Multilingual Natural Language Generation
Lecture Notes in Computer Science, 2002
We describe the symbolic authoring facilities of the M-PIRO project. M-PIRO is developing technology that allows personalized multilingual object descriptions, in both textual and spoken form, to be produced from symbolic information in a database and small fragments of text. The technology is being tested in the context of electronic museums, where a prototype that produces dynamically multilingual exhibit descriptions for presentations over the web has already been developed. This paper focuses on M-PIRO's authoring subsystem, which allows domain experts with no language technology expertise to configure the system for new applications. The authoring facilities allow the experts to define or modify the structure of the underlying database, its contents, and the system's domain-dependent linguistic resources. Previews of the generated texts can also be produced during the authoring process to monitor the content and quality of the resulting descriptions.
Natural Language Generation in Artificial Intelligence and Computational Linguistics
The Kluwer International Series in Engineering and Computer Science, 1991
A number of collections of papers from the field of natural language generation (NLG) have been published over the last few years: Kempen (1987), Zock and Sabah (1988), Dale, Mellish, and Zock (1990), and now the present volume. All have in common that they are derived in one way or another from workshops on the subject, and should therefore make available new and often exploratory research in a timely fashion. If such a book is to be more than a conference proceedings, it has to do a little more too, of course; it should present the research in more detail than a conference proceedings would, there should be greater cohesion amongst the papers, and it should be produced to an appropriate standard. The present book, like its predecessors, succeeds on some counts but fails on others. The papers in the book are organized into three strands, described in turn below: text planning, lexical choice, and grammatical resources. The balance between these is rather skewed, however: the first section contains eight papers, and the second and third contain only three papers each.
Multilingual natural language generation for multilingual software: A functional linguistic approach
Applied Artificial Intelligence, 1999
In this paper we present an implemented account of multilingual linguistic resources for multilingual text generation that improves significantly on the degree of re-use of resources both across languages and across applications. We argue that this is a necessary step for multilingual generation in order to reduce the high cost of constructing linguistic resources and to make NLG relevant for a wider range of applications-particularly, in this paper, for multilingual software and user interfaces. We begin by contrasting both a weak and a strong approach to multilinguality in the state of the art in multilingual text generation. Neither approach has provided sufficient principles for organizing multilingual work. We then introduce our framework where multilingual variation is included as an intrinsic feature of all levels of representation. We provide an example of multilingual tactical generation using this approach and discuss some of the performance, maintenance and development issues that arise.
Proceedings of the Linguistic Resources for Automatic Natural Language Generation - LiRA@NLG
2017
The Linguistic Resources for Automatic Natural Language Generation (LiRA@NLG) workshop of the International Natural Language Generation INLG2017 Conference held at Santiago de Compostela, September 4, 2017, brought together participants involved in developing large-coverage linguistic resources and researchers with an interest in expanding real-world Natural Language Generation (NLG) software. Linguists and developers of NLG software have been working separately for many years: NLG researchers are typically more focused on technical issues specific to text generation-where good performance (e.g. recall and precision) is crucial-whereas linguists tend to focus on problems related to the development of exhaustive and precise resources that are mainly 'neutral' visa -vis any NLP application (e.g. parsing or generating sentences), using various grammatical formalisms such as NooJ, TAG or HPSG. However, recent progress in both fields is reducing many of these differences, with largecoverage linguistic resources being more and more used by robust NLP software. For instance, NLG researchers can now use large dictionaries of multiword units and expressions, and several linguistic experiments have shown the feasibility of using large phrase-structure grammars (a priori used for text parsing) in 'generation' mode to automatically produce paraphrases of sentences that are described by these grammars. The eight papers presented at the LiRA@NLG workshop focused on the following questions: How do we develop 'neutral' linguistic resources (dictionaries, morphological, phrase-structure and transformational grammars) that can be used both to parse and generate texts automatically? Is it possible to generate grammatical sentences by using linguistic data alone, i.e. with no statistical methods to remove ambiguities? What are the limitations of rule-based systems, as opposed to stochastic ones? The common themes that these articles explore are: how to build large-coverage dictionaries and morphological grammars that can be used by NLG applications, how to integrate a linguistically-based Generation module into a Machine-Translation system, and how to construct a syntactic grammar that can be used by a transformational engine to perform paraphrase generation. Linguists as well as Computational Linguists who work on Automatic Generation based on linguistic methods will find advanced, up-to-the-minute studies on these topics in this volume: Max Silberztein's article, "Automatic Generation from FOAF to English: Linguistic Contribution to Web Semantics," presents an automatic system capable of generating a large number of English sentences from Friend Of A Friend (FOAF) statements in the RDF Turtle notation using NooJ's transformational engine both in Parse and Generation modes.