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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031021732
024	7		_a10.1007/978-3-031-02173-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.T48 _b2020 EB
100	1		_aNarayan, Shashi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687447
245	1	0	_aDeep Learning Approaches to Text Production _cby Shashi Narayan, Claire Gardent.
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XXIV, 175 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9141188 _aProceso de textos
700	1		_aGardent, Claire _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687448
776	0	8	_iPrinted edition: _z9783031001840
776	0	8	_iPrinted edition: _z9783031010453
776	0	8	_iPrinted edition: _z9783031033018
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02173-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI