國立高雄大學圖資館 |

Language: English

Back

Deep learning approaches to text pro...

Gardent, Claire,

Deep learning approaches to text production

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning approaches to text productionShashi Narayan, Claire Gardent.
Author:	Narayan, Shashi,
other author:	Gardent, Claire,
Description:	1 online resource (201 p.)
Subject:	Text processing (Computer science)
Online resource:	https://portal.igpublish.com/iglibrary/search/MCPB0006531.html
ISBN:	9781681737584

Deep learning approaches to text production
Narayan, Shashi,

Deep learning approaches to text production[electronic resource] /Shashi Narayan, Claire Gardent. - 1 online resource (201 p.) - Synthesis Lectures on Human Language Technologies ;44.

Includes bibliographical references (pages 139-173).

Deep learning approaches to text production -- Contents -- List of Figures -- List of Tables -- Preface -- Chapter 1: Introduction -- Part I: Basics -- Chapter 2: Pre-Neural Approaches -- Chapter 3: Deep Learning Frameworks -- Part II: Neural Improvements -- Chapter 4: Generating Better Text -- Chapter 5: Building Better Input Representations -- Chapter 6: Modelling Task-Specific Communication Goals -- Part III: Data Sets and Conclusion -- Chapter 7: Data Sets and Challenges -- Chapter 8: Conclusion -- Bibliography -- Authors' Biographies.

Access restricted to authorized users and institutions.

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.

Mode of access: World Wide Web.

ISBN: 9781681737584Subjects--Topical Terms:

210527
Text processing (Computer science)
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QA76.9.T48

Dewey Class. No.: 006.3

Deep learning approaches to text production
LDR:03234nmm a2200313 a 4500 001 603298
006 m eo d
007 cr cn |||m|||a
008 211117t20202020cau ob 000 0 eng d
020 $a 9781681737584
020 $a 9781681737591
020 $a 9781681737607
035 $a MCPB0006531
040 $a iG Publishing $b eng $c iG Publishing $e rda
050 0 0 $a QA76.9.T48
082 0 0 $a 006.3
100 1 $a Narayan, Shashi, $e author. $3 899560
245 1 0 $a Deep learning approaches to text production $h [electronic resource] / $c Shashi Narayan, Claire Gardent.
264 1 $a San Rafael, California : $b Morgan & Claypool Publishers, $c 2020.
264 4 $c ©2020
300 $a 1 online resource (201 p.)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
490 1 $a Synthesis Lectures on Human Language Technologies ; $v 44
504 $a Includes bibliographical references (pages 139-173).
505 0 $a Deep learning approaches to text production -- Contents -- List of Figures -- List of Tables -- Preface -- Chapter 1: Introduction -- Part I: Basics -- Chapter 2: Pre-Neural Approaches -- Chapter 3: Deep Learning Frameworks -- Part II: Neural Improvements -- Chapter 4: Generating Better Text -- Chapter 5: Building Better Input Representations -- Chapter 6: Modelling Task-Specific Communication Goals -- Part III: Data Sets and Conclusion -- Chapter 7: Data Sets and Challenges -- Chapter 8: Conclusion -- Bibliography -- Authors' Biographies.
506 $a Access restricted to authorized users and institutions.
520 3 $a 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.
538 $a Mode of access: World Wide Web.
650 0 $a Text processing (Computer science) $3 210527
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Machine learning $v Congresses. $3 384498
650 0 $a Artificial intelligence $3 252958
655 4 $a Electronic books. $2 local. $3 214472
700 1 $a Gardent, Claire, $e author. $3 899561
856 4 0 $u https://portal.igpublish.com/iglibrary/search/MCPB0006531.html