國立虎尾科技大學 |

Deep learning approaches to text production /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning approaches to text production // Shashi Narayan, Claire Gardent.
作者:	Narayan, Shashi,
其他作者:	Gardent, Claire,
面頁冊數:	1 online resource (201 p.)
標題:	Artificial intelligence -
電子資源:	https://portal.igpublish.com/iglibrary/search/MCPB0006531.html
ISBN:	9781681737584

Deep learning approaches to text production /
Narayan, Shashi,

Deep learning approaches to text production /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:

671585
Artificial intelligence
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.9.T48

Dewey Class. No.: 006.3

Deep learning approaches to text production /
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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.
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