Deep Learning Approaches to Text Production / by Shashi Narayan, Claire Gardent.

Format:

Book

Author/Creator:

Narayan, Shashi., Author.

Gardent, Claire, Author.

Series:

Synthesis Lectures on Human Language Technologies, 1947-4059

Language:

English

Subjects (All):

Artificial intelligence.

Natural language processing (Computer science).

Computational linguistics.

Artificial Intelligence.

Natural Language Processing (NLP).

Computational Linguistics.

Local Subjects:

Artificial Intelligence.

Natural Language Processing (NLP).

Computational Linguistics.

Physical Description:

1 online resource (XXIV, 175 p.)

Edition:

1st ed. 2020.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

Summary:

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.

Contents:

List 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.

ISBN:

9783031021732

3031021738