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Table of contents (8 chapters)
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Front Matter
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Basics
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Front Matter
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Neural Improvements
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Front Matter
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Data Sets and Conclusion
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Front Matter
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Back Matter
About this book
Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
Authors and Affiliations
About the authors
Claire Gardent is a research scientist at CNRS, the French National Center for Scientific Research. Prior to joining the CNRS, she worked at the Universite de Clermont-Ferrand (France), Saarbrucken Universitat (Germany), Utrecht, and Amsterdam Universiteit (The Netherlands). She received her Ph.D. from the University of Edinburgh and her M.Sc. from Essex University. She was nominated Chair of the EACL and acted as program chair for various international conferences, workshops, and summer schools (EACL, ENLG, SemDIAL, SIGDIAL, ESSLLI,*SEM). She served on the editorial board of the journals Computational Linguistics, Journal of Semantics and Traitement Automatique des Langues, recently headed the WebNLG project (Nancy,Bolzano, Stanford SRI), and acted as chair of SIGGEN, the ACL Special Interest Group in Natural Language Generation. She also co-organised the WebNLG Shared Task, a challenge on generating text from RDF data. Her research interests include executable semantic parsing, natural language generation, question answering, dialogue and the use of computational linguistics for linguistic analysis.
Bibliographic Information
Book Title: Deep Learning Approaches to Text Production
Authors: Shashi Narayan, Claire Gardent
Series Title: Synthesis Lectures on Human Language Technologies
DOI: https://doi.org/10.1007/978-3-031-02173-2
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 9
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-031-01045-3Published: 20 March 2020
eBook ISBN: 978-3-031-02173-2Published: 01 June 2022
Series ISSN: 1947-4040
Series E-ISSN: 1947-4059
Edition Number: 1
Number of Pages: XXIV, 175
Topics: Artificial Intelligence, Natural Language Processing (NLP), Computational Linguistics