Basile, Valerio

English

De grote droom van het vroege computertijdperk was de ontwikkeling van eenintelligente elektronische machine, die mensen kon begrijpen en met hen koncommuniceren door middel van hun eigen taal. Het werd echter al snelduidelijk dat computers niet binnen een paar jaar zover zouden zijn dat zemet mensen aan tafel konden zitten en een gesprek konden voeren. Hetvakgebied van mens-computercommunicatie had nog een lange weg te gaan.Natuurlijke taal, zoals het Nederlands of het Engels, is voor een computererg moeilijk te begrijpen vanwege het onsystematische en soms dubbelzinnigegebruik ervan. Daarom is het overgrote deel van het onderzoek naarnatuurlijke taalverwerking door computers gericht op het interpreteren vannatuurlijke taal, en niet op de productie ervan. Het resultaat hiervan isdat het genereren van goedgevormde taal gegeven een bepaalde betekenis eenonopgelost probleem is. Computerapplicaties die prompt tekst moetengenereren op basis van verschillende vormen van invoer zijn essentieel, maarmoeilijk om te implementeren.Het uiteindelijke doel van een generatiesysteem voor natuurlijke taal is omzowel simpele als complexe betekenis te kunnen overbrengen door middel vannatuurlijke taal. De eerste vraag die we moeten beantwoorden is echter hoedeze betekenis beschreven kan worden. Wij maken hiervoor gebruik vanDiscourse Representation Theory, een formalisme dat succesvol is geblekenbij het beschrijven van en redeneren over natuurlijke taal. De logischerepresentaties van dit formalisme zijn erg geschikt om door een computergeanalyseerd te worden. Dit proefschrift presenteert een modulair systeemdat vanuit deze logische representaties natuurlijke taal genereert.The dream of the early computer era was that of a more or less intelligentelectronic machine that understands its human counterpart and communicatesbidirectionally using their language. The history shows that expectingcomputers to be able to “sit at the table” and converse with humans in a fewyears was a naïve idea, and that the field of human-computer communicationhad a long way to go.For a computer it is not easy to “understand” the fuzzy, potentiallyambiguous natural language (e.g., English). In fact, the greatest portion,by far, of the research work in the field of Natural Language Processing(NLP) revolves around the analysis of language, rather than its production.All the while, the generation of proper expressions in natural language isan open problem. Applications that need to generate text on-the-fly based ona variety of possible inputs are essential but not easy to implement.The ultimate goal of a system that does Natural Language Generation is toconvey a meaning, simple or complex, through natural language. But how do wedescribe and represent meaning in the first place? Here, we make use of thelogical forms from Discourse Representation Theory, which has beensuccessfully employed to represent and reason about abstract meaning ina formal way. Logical forms are formal by definition, thus also directlytractable with computational methods. This thesis presents a modular systembased on computational methods to generate natural language expressions fromthese kind of input

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   University of GroningenFrom logic to languageBasile, ValerioIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2015Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Basile, V. (2015). From logic to language: Natural language generation from logical forms. University ofGroningen.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 08-06-2022STELLINGENbehorende bij het proefschriftFrom Logic to LanguageNatural Language Generation from Logical FormsvanValerio Basile1) Logical Forms are a good candidate for the role of input for Natural Language Generation, both for being a theoretically sound formalism, and for the practical implications.2) Discourse Representation Structures give the possibility to produce a surface form for each discourse referent separately, as well as the surface form for an entire sentence or even discourse, by virtue of composition.3) Discourse Representation Graphs retain the expressive power of Discourse Representation Structures, while simplifying the word-level alignment.4) The Groningen Meaning Bank provides a large enough quantity of text aligned with logical forms to be used as training material for supervised stochastic methods for NLG. The crowdsourcing approach to annotation ensures that its quality constantly improves over time.5) WordNet is a valuable resource for generating expressions of natural language, despite not being a full-fledged ontology, because it provides sets of lemmas for each concept to generate, thus transforming the problem of lexicalization into the simpler problem of lexical choice.6) The lexicalization of the concepts of a Discourse Representation Graph is choosing a lemma and generating the morphology.7) The task of predicting whether or not a part of a logical form participates in the generation process is easy, to order them correctly is hard, especially with respect to event referents.8) The evaluation of the output of a natural language generation pipeline based on string matching is too strict, counting as errors productions that are arguably acceptable. This applies to the evaluation of the word order as well as the choice of lexemes.9) Academic code is like a  frikandel: monolithic, mysterious, and hard to process.10) "Gezellig" is een gezellig woord.

From logic to language:Natural language generation from logical forms

https://pure.rug.nl/ws/files/26252480/Propositions.pdf

From logic to language:Natural language generation from logical forms

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