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008 220601s2020 sz | s |||| 0|eng d
020 _a9783031021732
_9978-3-031-02173-2
024 7 _a10.1007/978-3-031-02173-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aNarayan, Shashi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980700
245 1 0 _aDeep Learning Approaches to Text Production
_h[electronic resource] /
_cby Shashi Narayan, Claire Gardent.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXXIV, 175 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aList of Figures -- List of Tables -- Preface -- Introduction -- Pre-Neural Approaches -- Deep Learning Frameworks -- Generating Better Text -- Building Better Input Representations -- Modelling Task-Specific Communication Goals -- Data Sets and Challenges -- Conclusion -- Bibliography -- Authors' Biographies.
520 _aText 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aGardent, Claire.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980701
710 2 _aSpringerLink (Online service)
_980702
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001840
776 0 8 _iPrinted edition:
_z9783031010453
776 0 8 _iPrinted edition:
_z9783031033018
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980703
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02173-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85017
_d85017