Machine Translation: Its Scope and Limits

246 Pages·2009·2.868 MB·English

by  Yorick Wilks (auth.)

Machine Translation
Yorick Wilks
Machine Translation
Its Scope and Limits
123
YorickWilks
DepartmentofComputerScience
TheUniversityofSheffield
RegentCourt,211PortobelloStreet
Sheffield,S14DP,UK
[email protected]
ISBN:978-0-387-72773-8 e-ISBN:978-0-387-72774-5
LibraryofCongressControlNumber:2008931409
(cid:2)c SpringerScience+BusinessMediaLLC2009
Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten
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Foreword
This book is a set of essays covering aspects of machine translation (MT) past,
present and future. Some have been published before, some are new but, taken
together,theyaremeanttopresentacoherentaccountofthestateofMT,itsevolu-
tionuptothepresent,anditsscopeforthefuture.Atcertainpoints,“Afterwords”
havebeenaddedtocommentonthe,possiblychanged,relevanceofachapteratthe
timeofpublication.
The argument for reprinting here some older thoughts on MT is an attempt to
showsomecontinuityofoneresearcher’sthoughts,sofaraspossible,inthewelter
of argument and dispute that has gone on over decades on how MT is to be done.
Thebookiscertainlynotintendedasacomprehensivehistoryofthefield,andthese
already exist. Nor is any one MT system described or advocated here. The author
hasbeeninvolvedintheproductionofthreequitedifferentsystems:atoysemantics-
basedsystematStanfordin1971,whosecodewasplacedintheComputerMuseum
inBostonasthefirstmeaning-drivenMTsystem.Later,in1985,Iwasinvolvedin
New Mexico in ULTRA, a multi-language system with strong semantic and prag-
matic features, intended to show that the architecture of the (failed) EUROTRA
systemcouldperformbetterat1%ofwhatthatcost.Nocomprehensivedescription
of EUROTRA is given here, and the history of that project remains to be written.
Lastly,in1990,IwasoneofthreePIsintheDARPA-fundedsystemPANGLOSS,
a knowledge-based system set up in competition with IBM’s CANDIDE system,
that became the inspiration for much of the data-driven changes that have over-
taken language processing since 1990. None of these systems is being presented
here as a solution to MT, for nothing yet is, but only as a test-bed of ideas and
performance.
Machine translation is not, as some believe, solved, nor is it impossible, as
others still claim. It is a lively and important technology, whose importance in
a multi-lingual and information-driven world can only increase, intellectually and
commercially.Intellectually,itremains,asitalwayshasbeen,theultimatetestbed
ofalllinguisticandlanguageprocessingtheories.
In writing this book, I am indebted to too many colleagues and students to
mention, though I must acknowledge joint work with David Farwell (chapters 10
v
vi Foreword
and 14), and Sergei Nirenburg, Jaime Carbonnel and Ed Hovy (chapter 8). I also
need to thank Lucy Moffatt for much help in its preparation, and Roberta, for
everything,asalways.
Sheffield,2008 YorickWilks
History Page
Somechaptershaveappearedinotherformselsewhere:
Chapter 2: Wilks, Y. (1984) Artificial Intelligence and Machine Translation. In
S. and W. Sedelow (eds.) Current Trends in the Language Sciences. Amsterdam:
NorthHolland.
Chapter3:Wilks,Y.(1973)AnArtificialIntelligenceApproachtoMachineTransla-
tion.InR.SchankandK.Colby(eds.)ComputermodelsofThoughtandLanguage.
SanFrancisco:Freeman.
Chapter4:Wilks,Y.(1992)SYSTRAN:itobviouslyworks,buthowmuchcanitbe
improved?InJ.Newton(ed.)ComputersandTranslation.London:Routledge.
Chapter 7: Wilks, Y. (1994) Developments in machine translation research in
the US. In the Aslib Proceedings, Vol. 46 (The Association of Information
Management).
vii
Contents
1 Introduction ................................................... 1
PartI MTPast
2 FiveGenerationsofMT ......................................... 11
3 AnArtificialIntelligenceApproachtoMachineTranslation ......... 27
4 It Works but How Far Can It Go: Evaluating the SYSTRAN
MTSystem .................................................... 65
PartII MTPresent
5 Where Am I Coming From: The Reversibility of Analysis
andGenerationinNaturalLanguageProcessing ................... 89
6 What are Interlinguas for MT: Natural Languages, Logics
orArbitraryNotations?......................................... 97
7 StoneSoupandtheFrenchRoom:TheStatisticalApproachtoMT
atIBM ........................................................101
8 TheRevivalofUSGovernmentMTResearchin1990...............115
ix
x Contents
9 TheRoleofLinguisticKnowledgeResourcesinMT ................125
10 TheAutomaticAcquisitionofLexiconsforanMTSystem...........139
PartIII MTFuture
11 SensesandTexts ...............................................157
12 SenseProjection................................................169
13 LexicalTuning .................................................177
14 WhatWouldPragmatics-BasedMachineTranslationbeLike? ......195
15 WherewasMTattheEndoftheCentury:WhatWorksandWhat
Doesn’t?.......................................................215
16 TheFutureofMTintheNewMillennium.........................225
References.........................................................237
Index .............................................................247
Part I
MT Past
Chapter2
Five Generations of MT
Introduction(2007)
ThereisanancientChinesecursethatdoomsrecipientstoliveinaninterestingage,
and by those standards MT workers are at present having a bad time. The reason
thingsareinterestingatthemomentisthatthereisanumberofconflictingclaims
in the air about how to do MT, and whether it can, or indeed has already, been
done. Such a situation is unstable, and we may confidently expect some kind of
outcome–alwayscheeringfortheempiricist–inthenearfuture.Thischapterwas
initially published as (Wilks, 1979) as an exploration of the relationship of MT to
ArtificialIntelligence(AI)ingeneral,andshouldbeseenasprovidingasnapshotof
thatintellectualperiod.
Whathappenedinitiallywasthreefold.First,the“bruteforce”methodsforMT,
thatwerethoughttohavebeenbroughttoanendbytheALPAC(1966)Reporthave
surfaced again, like some Coelacanth from the deep, long believed extinct. Such
systems were sold for many years under such trade names as LOGOS, XYZYX,
SMART,WeidnerandSYSTRAN;andthelast,andbestknown,hasbeenusedfor
thirtyyearsbytheEUinParis(VanSlype,1976)andLuxembourg.
Secondly, some large-scale, more theoretically based, MT projects continued,
usually based in Universities, and have been tested in use, though sometimes on
a scale smaller than that originally envisaged. METEO, for example, in Montreal
(Chandioux, 1976), which was to have translated official documents from English
to French, is still in use for the translation of the more limited world of weather
reports.
Thirdly,workersinnaturallanguageinthefieldknownasArtificialIntelligence
(AI)begantomakedistinctclaimsabouttheneedfortheirapproachifthereisever
tobegeneralandhighqualityMT(Wilks,1973a;Charniak,1973;Schank,1975a).
Smallpilotsystemsillustratingtheirclaimswereprogrammed,buttheirroleinMT
discussionwasmainlyofatheoreticalnature.
However,thesearenotmerelythreecomplementaryapproaches,fortheyseemto
bemakingdifferentclaims,and,unlesswetaketheeasywayoutandsimplydefine
somelevelofMTappropriatetoeachoftheenterprises,itseemstheycannotallbe
right,andthatwemayhopeforsomeresolutionbeforetoolong.
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