Machine Translation

189 Pages·2017·1.35 MB·English

by  Thierry Poibeau

The MIT Press Essential Knowledge Series Auctions,
Timothy P. Hubbard and Harry J. Paarsch Cloud
Computing, Nayan Ruparelia Computing: A Concise
History, Paul E. Ceruzzi The Conscious Mind, Zoltan L.
Torey Crowdsourcing, Daren C. Brabham
Free Will, Mark Balaguer
Information and Society, Michael Buckland Information and the Modern
Corporation, James W. Cortada Intellectual Property Strategy, John Palfrey The
Internet of Things, Samuel Greengard Machine Learning: The New AI, Ethem
Alpaydin Machine Translation, Thierry Poibeau Memes in Digital Culture,
Limor Shifman Metadata, Jeffrey Pomerantz
The Mind–Body Problem, Jonathan Westphal MOOCs, Jonathan Haber
Neuroplasticity, Moheb Costandi
Open Access, Peter Suber
Paradox, Margaret Cuonzo
Robots, John Jordan
Self-Tracking, Gina Neff and Dawn Nafus Sustainability, Kent E. Portney
The Technological Singularity, Murray Shanahan Understanding Beliefs, Nils J.
Nilsson Waves, Frederic Raichlen
Machine Translation
Thierry Poibeau

The MIT Press
Cambridge, Massachusetts
London, England
© 2017 Massachusetts Institute of Technology All rights reserved. No part of this book may be
reproduced in any form by any electronic or mechanical means (including photocopying, recording, or
information storage and retrieval) without permission in writing from the publisher.
This book was set in Chaparral Pro by Toppan Best-set Premedia Limited. Printed and bound in the
United States of America.
Library of Congress Cataloging-in-Publication Data is available.
ISBN: 978-0-262-53421-5
eISBN 9780262342438
ePub Version 1.0
Table of Contents
Series page
Title page
Copyright page
Series Foreword
Acknowledgments
1 Introduction
2 The Trouble with Translation
3 A Quick Overview of the Evolution of Machine Translation
4 Before the Advent of Computers…
5 The Beginnings of Machine Translation: The First Rule-Based Systems
6 The 1966 ALPAC Report and Its Consequences
7 Parallel Corpora and Sentence Alignment
8 Example-Based Machine Translation
9 Statistical Machine Translation and Word Alignment
10 Segment-Based Machine Translation
11 Challenges and Limitations of Statistical Machine Translation
12 Deep Learning Machine Translation
13 The Evaluation of Machine Translation Systems
14 The Machine Translation Industry: Between Professional and Mass-
Market Applications
15 Conclusion: The Future of Machine Translation
Glossary
Bibliography and Further Reading
Index
About Author
List of Tables
Table 1 Example of possible translations in French for the English word
“motion”
List of Illustrations
Figure 1 The Necker cube, the famous optical illusion published by Louis
Albert Necker in 1832. (Image licensed under CC BY-SA 3.0 via Wikimedia
Commons. From
https://commons.wikimedia.org/wiki/File:Necker_cube.svg.)
Figure 2 Vauquois’ triangle (image licensed under CC BY-SA 3.0, via
WikiMedia Commons). Source:
https://en.wikipedia.org/wiki/File:Direct_translation_and_transfer_translation_pyramind.svg.
Figure 3 An extract from the Hansard corpus aligned at sentence level.
Figure 4 Two texts of different length. Each cell with a number n
corresponds to a sentence of length n.
Figure 5 Beginning of alignment based on sentence length.
Figure 6 Other possible simple alignments.
Figure 7 Alignment of remaining sentences.
Figure 8 Two texts in a translation situation. Although the content of the
texts is unknown (here represented by “xxx” and “yyy”), some words are
identical or similar and can help determine reliable correspondence points.
Figure 9 Automatically extracted sentences from a bilingual corpus in order
to translate the sentence “training is not the solution to every problem.” Each
sentence in English contains a sequence of n similar words with the sentence
to be translated.
Figure 10 Different examples with the Japanese particle “no.” One can see
that the particle requires the use of a different linguistic structure each time
when translating into English, depending on the context (see Sumita and
Iida, 1991).
Figure 11 A possible alignment between two sentences.
Figure 12 A possible alignment between two sentences, with several
intersecting links.
Figure 13 Initialization of the alignments. Each English word is linked with
equal probability to all the words in the French translation.
Figure 14 After the first iteration, the algorithm identifies the link between
“la” and “the” as being the most likely, based on their frequency in the
source language and in the target language. These links are strengthened
(shown in bold) to the detriment of other links and therefore also other
possible alignments.
Figure 15 After another iteration, the algorithm identifies the other most
probable links between “voiture” and “car,” then between “chaise” and
“chair” and between “red” and “rouge.” The other possible links and
alignments become less and less probable.
Figure 16 The process ends when there is convergence, meaning a stable
structure has been found. The other links in the figure are removed, but in
fact they remain available with a very low probability. It is possible to filter
the alignment using a threshold in order to select only a limited number of
possibilities, as shown in this figure, where the alternative links have been
completely deleted.
Figure 17 Example of an alignment that is impossible to obtain from IBM
models. The sequence “don’t have any money” corresponds to the group
“sont démunis” in French: this is an example of an m-n correspondence
(here, m=4 and n=2 such that four English words correspond to two French
words, if we consider “don’t” as a single word).
Figure 18 Segment-based translation: different segments have been found
corresponding to isolated words or to longer sequences of words. The system
then has to find the most probable translation from these different pieces of
translation. It is probable that “les pauvres n’ont pas d’argent” will be
preferred to “les pauvres sont démunis,” but this would be acceptable since
the goal of automatic systems is to provide a literal translation, not a literary
one.
Figure 19 Performance obtained with the same standard statistical translation
system applied over 22 different European languages. The translation system
is based on the standard Moses toolbox, the corpus used is the JRC-Acquis
corpus (see chapter 7), and the metric used is the BLEU score. Dark grey
cells correspond to a BLEU score performance over 0.5, and light grey cells
to a BLEU score performance under 0.4 (blank: between 0.4 and 0.49).
Language abbreviations: bg: Bulgarian; cs: Czech; da: Danish; de: German;
el: Greek; en: English; es: Spanish; et: Estonian; fi: Finnish; fr: French; gr:
Greek; hu: Hungarian; it: Italian; lt: Lithuanian; lv: Latvian; mt: Maltese; nl:
Dutch; pl: Polish; pt: Portuguese; ro: Romanian; sk: Slovak; sl: Slovene; sv:
Swedish (note that et, fi, and hu are Finno-Ugric, mt is Semitic, and all other
languages are Indo-European). Figure taken from Koehn et al., 2009.
Reproduced with the authorization of the authors.
Figure 20 Variations of the word “book” in Finnish, depending on its
grammatical function.
Series Foreword
The MIT Press Essential Knowledge series offers accessible, concise, beautifully
produced pocket-size books on topics of current interest. Written by leading
thinkers, the books in this series deliver expert overviews of subjects that range
from the cultural and the historical to the scientific and the technical.
In today’s era of instant information gratification, we have ready access to
opinions, rationalizations, and superficial descriptions. Much harder to come by
is the foundational knowledge that informs a principled understanding of the
world. Essential Knowledge books fill that need. Synthesizing specialized
subject matter for nonspecialists and engaging critical topics through
fundamentals, each of these compact volumes offers readers a point of access to
complex ideas.

Bruce Tidor Professor of Biological Engineering and Computer Science
Massachusetts Institute of Technology
Acknowledgments
This book would not have been possible without the support of colleagues and
friends. I want to thank Michelle Bruni, Elizabeth Rowley-Jolivet, Pablo Ruiz
Fabo, and Bernard Victorri for their help during the preparation of this book. My
gratitude also goes to the editorial and production staff at MIT Press, particularly
Marie Lufkin Lee and Katherine A. Almeida. Finally, I want to thank the
anonymous reviewers for their careful reading and their many insightful
comments and suggestions.
Thierry Poibeau is a member of LATTICE, a research laboratory supported by
CNRS, Ecole normale supérieure (ENS), PSL Research University, Université
Sorbonne nouvelle, and USPC.