Discourse Processing
Manfred Stede
2011
Bitext Alignment
Jörg Tiedemann 2011
Linguistic Structure Prediction
Noah A. Smith
2011
Learning to Rank for Information Retrieval and Natural Language Processing
Hang Li
2011
Computational Modeling of Human Language Acquisition
Afra Alishahi
2010
Introduction to Arabic Natural Language Processing
Nizar Y. Habash
2010
Cross-Language Information Retrieval
Jian-Yun Nie
2010
Automated Grammatical Error Detection for Language Learners
Claudia Leacock, Martin Chodorow, Michael Gamon, and Joel Tetreault
2010
Data-Intensive Text Processing with MapReduce
Jimmy Lin and Chris Dyer
2010
Semantic Role Labeling
Martha Palmer, Daniel Gildea, and Nianwen Xue
2010
Spoken Dialogue Systems
Kristiina Jokinen and Michael McTear
2009
Introduction to Chinese Natural Language Processing
Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang
2009
Introduction to Linguistic Annotation and Text Analytics
Graham Wilcock
2009
Dependency Parsing
Sandra Kübler, Ryan McDonald, and Joakim Nivre
2009
Statistical Language Models for Information Retrieval
ChengXiang Zhai
2008
Copyright © 2020 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Deep Learning Approaches to Text Production
Shashi Narayan and Claire Gardent
www.morganclaypool.com
ISBN: 9781681737584 paperback
ISBN: 9781681737591 ebook
ISBN: 9781681738215 epub
ISBN: 9781681737607 hardcover
DOI 10.2200/S00979ED1V01Y201912HLT044
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES
Lecture #44
Series Editor: Grame Hirst, University of Toronto
Series ISSN
Print 1947-4040 Electronic 1947-4059
Deep Learning Approachesto Text Production
Shashi Narayan
University of Edinburgh
Claire Gardent
CNRS/LORIA, Nancy
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #44
ABSTRACT
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.
KEYWORDS
text production, text generation, deep learning, neural networks, meaning-to-text, data-to-text, text-to-text, recurrent neural networks, sequence-to-sequence models, attention, copy, coverage, AMR generation, RDF generation, verbalise, simplification, compression, paraphrasing, dialogue generation, summarisation, content selection, adequacy, input understanding, sentence representation, document representation, communication goals, deep generators, reinforcement learning, evaluation, grammatical, fluent, meaning-preserving, BLEU, ROUGE, relevant, coherent
To my family.
– Shashi
A mes trois rayons de soleil,
