Machine Translation: Past, Present and Future (курсова)

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“Machine Translation: Past, Present and Future”



Machine Translation: The First 40 Years, 1949-1989

Machine Translation in 1990s

Machine Translation Quality

Machine Translation and Internet

Machine and Human Translation

Concluding remarks

Literature used


Now it is time to analyze what has happened in the 50 years since
machine translation began, review the present situation, and speculate
on what the future may bring. Progress in the basic processes of
computerized translation has not been as striking as developments in
computer technology and software. There is still much scope for the
improvement of the linguistic quality of machine translation output,
which hopefully developments in both rule-based and corpus-based methods
can bring. Greater impact on the future machine translation scenario
will probably come from the expected huge increase in demand for on-line
real-time communication in many languages, where quality may be less
important than accessibility and usability.

Machine Translation: The First 40 Years, 1949-1989

About fifty years ago, Warren Weaver, a former director of the division
of natural sciences at the Rockefeller Institute (1932-55), wrote his
famous memorandum which had launched research on machine translation at
first primarily in the United States but before the end of the 1950s
throughout the world.

In those early days and for many years afterwards, computers were quite
different from those that we have today. They were very expensive
machines disposed in large rooms with reinforced flooring and
ventilation systems to reduce excess heat. They required a huge number
of maintenance engineers and a dedicated staff of operators and
programmers. Most of the work was mathematical in fact, either directly
for military institutions or for university departments of physics and
applied mathematics with strong links to the armed forces. It was
perhaps natural in these circumstances that much of the earliest work on
machine translation was supported by military or intelligence funds
directly or indirectly, and was destined for usage by such organizations
– hence the emphasis in the United States on Russian-to-English
translation, and in the Soviet Union on English-to-Russian translation.

Although machine translation attracted a great deal of funding in the
1950s and 1960s, particularly when the arms and space races began in
earnest after the launch of the first satellite in 1957, and the first
space flight by Gagarin in 1961, the results of this period of activity
were disappointing. US was even going to close the research after the
publication of the shattering ALPAC (Automatic Language Processing
Advisory Committee) report (1966) which concluded that the United States
had no need of machine translation even if the prospect of reasonable
translations were realistic – which then seemed unlikely. The authors of
the report had compared unfavourably the quality of the output produced
by current systems with the artificially high quality of the first
public demonstration of machine translation in 1954 – the
Russian-English program developed jointly by IBM and Georgetown
University. The linguistic problems encountered by machine translation
researchers had proved to be much greater than anticipated, and that
progress had been painfully slow. It should be mentioned that just over
five years earlier Joshua Bar-Hillel, one of the first enthusiasts for
machine translation who had been disabused of his work, had published
his critical review of machine translation research in which he had
rejected the implicit aim of fully automatic high quality translation
(FAHQT). Indeed he provided a proof of its “non-feasibility”. The
writers of the ALPAC report agreed with this diagnosis and recommended
that research on fully automatic systems should stop and that attention
should be directed to lower-level aids for translators.

For some years after ALPAC, research continued on a much-reduced
financing. By the mid 1970s, some success could be shown: in 1970 the US
Air Force began to use the Systran system for Russian-English
translations, in 1976 the Canadians began public use of weather reports
translated by the Meteo sublanguage machine translation system, and the
Commission of the European Communities applied the English-French
version of Systran for helping it with its heavy translation burden –
which soon was followed by the development of systems for other European
languages. In the 1980s, machine translation rose from its post-ALPAC
low spirits: activity began again all over the world – most notably in
Japan – with new ideas for research (particularly on knowledge-based and
interlingua-based systems), new sources of financial support (the
European Union, computer companies), and in particular with the
appearance of the first commercial machine translation systems on the

Initially, however, attention to the renewed activity was still almost
focuses on automatic translation with human assistance, both before
(pre-editing), during (interactive solution of problems) and after
(post-editing) the translation process itself. The development of
computer-based aids or tools for use by human translators was still
relatively neglected – despite the explicit requests of translators.

Nearly all research activities in the 1980s were devoted to the
exploration of methods of linguistic analysis in order to create
generation of programs based on traditional rule-based transfer and
interlingua (AI-type knowledge bases representing the more innovative
tendency). The needs of translators were left to commercial interests:
software for terminology management became available and ALPNET produced
a series of translator tools during the 1980s – among them it may be
noted was an early version of a program “Translation Memory” (a
bilingual database).

Machine Translation in 1990s

The real emergence of translator aids came in the early 1990s with the
“translator workstation”, among them were such programs as “Trados
Translator Workbench”, “IBM Translation Manager 2”, “STAR Transit”,
“Eurolang Optimizer”, which combined sophisticated text processing and
publishing software, terminology management and translation memories.

In the early 1990s, research on machine translation was reinforced by
the coming of corpus-based methods, especially by the introduction of
statistical methods (“IBM Candide”) and of example-based translation.
Statistical (stochastic) techniques have brought a reliase from the
increasingly evident limitations and inadequacies of previous
exclusively rule-based (often syntax-oriented) approaches. Problems of
disambiguation, refraining from repetition and more idiomatic generation
have become more solvable with corpusbased techniques. On their own,
statistical methods are no more the answer in contrast to rule-based
methods, but there are now prospects of improved output quality which
did not seem reachable 15 years ago. As many observers have indicated,
the most promising approaches will probably integrate rule-based and
corpus-based methods. Even outside research environments integration is
already evident: many commercial machine translation systems now
incorporate translation memories, and many translation memory systems
are being enriched by machine translation methods.

The main feature of the 1990s has been the rapid increase in the use of
machine translation and translation tools. The globalization of commerce
and information is placing increasing demands upon the provision of
translations. It means not only continuing (maybe even accelerating)
growth of the use by multinational companies and translation services of
systems to assist in the production of good quality documentation in
many languages – by the use of machine translation and translation
memory systems or by multilingual document authoring systems, or by
combinations of both. Until recent times, the production of translations
has been seen essentially as a self-contained activity. For large users,
the appearance of translation systems has stimulated the integration of
translation and documentation (technical writing and publishing)
processes. Translation is now seen as one stage in the processes of
communication and getting information. Future products for such kind
will not be separate independent machine translation systems, translator
workstations or translation tools, but multilingual documentation
software complexes combining document creation, translation and
revision, document archiving, information analysis, restoration and
extraction, etc. in order to satisfy the specific needs of companies.

Machine Translation Quality

Despite the prospects for the future, it has to be said that the new
approaches of the present have not yet resulted notable improvements in
the quality of the raw output by translation systems. These improvements
may come in the future, but overall it has to be said that at present
the actual translations produced do not represent major advances on
those made by the machine translation systems of the 1970s. We still see
the same errors: wrong pronouns, wrong prepositions, anomalous syntax,
incorrect choice of terms, plurals instead of singulars, wrong tenses,
etc. – errors that no human translators would ever commit.
Unfortunately, this situation probably won’t change in the near future.
There is little sign that basic generalpurpose machine translation
programs are soon going to show significant advances in translation
quality. And I think that if producers of machine translating systems
are still to continue sating market with software of low quality (as in
present) the whole machine translation industry may be condemned for
ever by the general public as producers of essentially poor-quality
software, that could possibly cause damaging of the research and
development or even its closure.

In order not to be unsubstantiated I would like to present examples of
translation by the programs of machine translation which are the most
widely distributed in Ukraine – “Promt” and “Magic Gooddy” (same
producer), “Pragma”, “Socrat” and one web-resource which provides
on-line real-time translation. Their work will be presented on the basis
of translation of the extract from the British newspaper article:

The Sunday Times:

Egypt has been training British MI5 and MI6 agents in how to combat
Islamic terrorists, underlining Cairo’s growing importance to the war
against terror and the Middle East peace process.

A senior Middle Eastern military intelligence official revealed last
week that British officers had undergone the training as part of a
co-operation programme with Egypt that began after the September 11
attacks on America in 2001 and continued until last year.

Details have not been revealed, but it is believed to have included
instruction in specialised interrogation techniques and in the
terminology used by terrorists, which will enable agents to understand
monitored telephone conversations.

Promt XT (Magic Gooddy):

Египет обучил британский MI5 и MI6 агентов при том, как сразиться с
Исламскими террористами, подчеркивая важность роста Каира к войне против
ужаса{террора} и ближневосточного мирного процесса.

Старшее Ближневосточное военное должностное лицо сведений{интеллекта}
показало на прошлой неделе, что британские чиновники{офицеры}
подверглись обучению как часть программы сотрудничества с Египтом,
который начал после 11 сентября нападения на Америку в 2001 и
продолжался до прошлого года.

Детали не были показаны, но это, как полагают, включило инструкцию в
специализированные методы допроса и в терминологию, используемую
террористами, которые позволят агентам понять проверенные телефонные


Египет готовил British MI5 и агентов MI6 в как, чтобы бороться
террористов Islamic, подчеркивающих Каир растущего значения в войну
против террора и мирный процесс Среднего Востока.

Старший Средний Восточный военный чиновник разведки обнаруживал прошлую
неделю, которую Британские должностные лица подверглись подготовку как
часть программы сотрудничества с Египтом, который начинался после
Сентябрьские 11 атак в Америке в 2001 и продолженной до прошлого года.

Детали не обнаружены, но поверено включенная инструкция в specialised
методах опроса и в терминологии использованной террористами, которые
позволят, чтобы понимать проверенную телефонную беседу.


Египет обучает Британский MI5 и агенты MI6 в том, как бороться с
Мусульманскими террористами, подчеркивая Cаiro’s, дорастающий важность
война против террора и процесса мира Ближнего Востока.

Ответственный Средний Восточный военный работник сведений, показанный
на последней неделе, что Британские офицеры имели undergone обучение,
так как часть программы сотрудничества с Египтом, который начался после
11 сентября, нападает на Америке в 2001 и длительный пока прошлый год.

Детали не были показаны, но это, как полагается, включало инструкцию в
speciаlised методах допроса и в терминологии, используемой террористами,
которые предоставят возможность агентам понять контролируемые телефонные

Internet machine translator www.tranexp.com:

Египетский вспомогательный глагол для образования сложных времен быть
воспитывать Британский MI5 и MI6 действующая сила в как к бой
Мусульманский террорист, подчеркивать Пирамида из камней рост
значительность к грамматический определенный член война от ужас и
грамматический определенный член Середина Восток мир процесс. ВЫСШАЯ
ОТМЕТКА ЗА КЛАССНУЮ РАБОТУ старший Середина Восточный военный ум
служебный открывать последний неделя тот Британский чиновник
вспомогательный глагол для образования сложных времен испытывать
грамматический определенный член воспитывать как часть яние) от высшая
отметка за классную работу co – действие программа с Египетский тот
начинать за грамматический определенный член Сентябрь 11 атаковать на
Американский в 2001 и непрерывный до прошлый год. Подробность
вспомогательный глагол для образования сложных времен не быть открывать,
только он быть верить к вспомогательный глагол для образования сложных
времен заключать обучение в специализация вопрос техника и в
грамматический определенный член терминология употребление у террорист,
который воля давать возможность или право действующая сила к понимать
наставник телефон разговор.

Literary translation:

Египет обучал агентов пятого и шестого отделов Британской военной
разведки методам борьбы с исламскими террористами, тем самым, подчеркнув
растущую значимость Каира в мирном процессе на Ближнем Востоке и борьбе
с террором.

Старшее должностное лицо Ближневосточной военной разведки обнародовал
секретные данные о том, что Британские офицеры прошли курс подготовки в
качестве части программы сотрудничества с Египтом, которая началась
вскоре после атак на Америку 11 сентября 2001 года и продолжалась до
прошлого года.

Детали не разглашались, однако считается, что они прошли курс обучения
специальным техникам допроса и терминологии используемой террористами,
который позволит агентам расшифровывать перехваченные телефонные

No doubt that the most appropriate translation was made by “Promt”, but
still its producer Russian company “ПРОект МТ” shouldn’t stop on

Machine Translation and Internet

The impact of the Internet has been significant in recent years. We are
already seeing an accelerating growth of real-time on-line translation
on the Internet itself. In recent years, we have seen many systems
designed specifically for the translation of Web pages (“Pop-Up
Dictionary”, “Site Translator”) and of electronic mail (“SKIIN”). The
demand for immediate translations will surely continue to grow rapidly,
but at the same time users are also going to want better results. There
is clearly an urgent need for translation systems developed specifically
to deal with the kind of colloquial (often wrongly formed and badly
spelled) messages found on the Internet. The old linguistics rule-based
approaches are probably not equal to the task on their own, and
corpusbased methods making use of the massive data available on the
Internet itself are obviously appropriate. But as yet there has been
little research on such systems. At the same time as we are seeing this
growing demand for “crummy” translations, the Internet is also providing
the means for more rapid delivery of quality translation to individuals
and to small companies. A number of machine translation systems on the
sale are already offering translation services, usually “adding value”
by human post-editing. More will surely appear as the years go by.

However, the Internet is having further profound impacts that will
surely change the future prospects for machine translation. There are
predictions that the stand-alone PC with its array of software for
word-processing, databases and games will be replaced by Network
Computers which would download systems and programs from the Internet at
any time as required. In this scenario, the one-off purchase of
individually packaged machine translation software or dictionaries would
be replaced by remote stores of machine translation programs,
dictionaries, grammars, translation archives or specialized glossaries
which would obviously be paid for according to usage. It is should be to
said, that such a change would have profound effect on the way in which
machine translation systems are developed.

Another profound impact of the Internet will concern the nature of the
software itself. What users of Internet services are seeking is
information in whatever language it may have been written or stored.
Users will want a seamless integration of information retrieval,
extraction and summarization systems with translation

In fact, it is possible that in next years there will be fewer “pure”
machine translation systems (commercial or on-line) and many more
computer-based tools and applications in which automatic translation is
just one component. As a first step, it will surely not be long before
all word-processing software includes translation as an in-built option.
Integrated language software will be the norm not only for the
multinational companies but also available and accessible for anyone
from their own computer (desktop, laptop, notebook or network-based
server) and for any device like television or mobile telephone which
interfacing with computer networks.

Spoken Language Translation

The most widely anticipated development of the next decade must be that
of speech translation. When current research projects (ATR, C-STAR,
JANUS, Verbmobil) were begun in the late 1980s and early 1990s, it was
known that practical applications were unlikely before the next century.
The limitation of these systems to small domains has clearly been
essential for any progress, such are the complexities of the task; but
these limitations mean that, when practical demonstrations are made,
observers will want to know when broader coverage will be realizable.
There is a danger here that the mistakes of the 1950s and 1960s might be
repeated; then, it was assumed that once basic principles and methods
had been successfully demonstrated on small-scale research systems it
would be merely a question of finance and engineering to create large
practical systems. The truth was otherwise; large-scale machine
translation systems have to be designed as such from the beginning, and
that requires many man-years of effort. It is still true to say that the
best written-language machine translation systems of today are the
outcome of decades of research and development.

Whatever the high expectations, it is surely unlikely that we will see
practical speech translation of significantly large domains for
commercial exploitation for another twenty years or more. Far more
likely, and in line with general trends within the field of written
language machine translation, is that there will be numerous
applications of spoken language translation as components of
small-domain natural language applications, e.g. interrogation of
databases (particularly financial and stockmarket data), interactions in
business negotiations or intra-company communication.

Machine and Human Translation

In the past there has often been tension between the translation
profession and those who advocate and research computer-based
translation tools. But now at the beginning of the 21-st century it is
already apparent that machine translation and human translation can and
will co-exist in relative harmony. Those skills which the human
translator can contribute will always be in demand.

Where translation has to be of “publishable” quality, both human
translation and machine translation perform their roles. Machine
translation is demonstrably cost-effective for large scale and/or rapid
translation of (boring) technical documentation, (highly repetitive)
software localization manuals, and many other situations where the costs
of machine translation plus essential human preparation and revision or
the costs of using computerized translation tools are significantly less
than those of traditional human translation with no computer aids. By
contrast, the human translator is (and will remain) unrivalled for
non-repetitive linguistically sophisticated texts (in literature or
law), and even for one-off texts in specific highly-specialized
technical subjects.

For the translation of texts where the quality of output is much less
important, machine translation is often an ideal solution. For example,
to produce “rough” translations of scientific and technical documents
that may be read by only one person who wants to find out only the
general content and information and is unconcerned whether everything is
intelligible or not, and who is certainly not discouraged by stylistic
awkwardness or grammatical errors, machine translation will increasingly
be the only appropriate decision. In general, human translators are not
prepared (and may resent being asked) to produce such “rough”
translations. In such a case the only alternative to machine translation
is no translation at all.

However, as I have already mentioned, greater familiarity with “crummy”
translations will inevitably stimulate demand for the kind of good
quality translations which only human translators can satisfy.

For the one-to-one interchange of information, there will probably
always be a role for the human translator, that is for the translation
of business correspondence (particularly if the content is sensitive or
legally binding). But for the translation of personal letters, machine
translation systems are likely to be increasingly used; and, for e-mail
and for the extraction of information from Web pages and computer-based
information services, machine translation is the only feasible solution.

As for spoken translation, there must surely always be a place for the
human translator. There can be no prospect of automatic translation
replacing the interpreter of diplomatic negotiations.

Finally, machine translation systems are opening up new areas where
human translation has never featured: the production of draft versions
for authors writing in a foreign language, who need assistance in
producing an original text; the real-time on-line translation of
television subtitles; the translation of information from databases;
and, no doubt, more such new applications will appear in the future as
the global communication networks expand and as the realistic usability
of machine translation (however poor in quality compared with human
translation) becomes familiar to a wider public.

Concluding remarks

Different electronic devices have become common nowadays. Taking
information from foreign languages with the help of different electronic
devices represents quite a new approach in modern translation practice.
Due to the fundamental research in the systems of algorithms and in the
establishment of lexical equivalence in different strata of lexicon,
machine translation has made considerable progress in recent years.
Nevertheless, its usage remains restricted in scientific, technological,
lexicographic realms. That is because machine translation can be
performed only on the basis of programmes worked out by linguistically
trained operators. Besides, the process of preparing programmes for any
matter is inseparably connected with great difficulties and takes much
time, whereas the quality of translation is far from being satisfactory
even at the lexical level, which have direct equivalent lexemes in the
target language. Considerably greater difficulties, which are
insurmountable for machine translation programs, present morphological
elements like prefixes, suffixes, endings, etc. Syntactic units (word
combinations, sentences) with various means of connection between their
components are also great obstacles for machine translation. Moreover,
modern electronic devices which perform translation do not possess the
necessary lexical, grammatical and stylistic memory to provide the
required standard of correct literary translation. Hence, the frequent
violations of syntactic agreement and government between the parts of
the sentence in machine translated texts. Very often the machine
translation program can not select in its memory the correct order of
words in word-combinations and sentences in the target language. And as
a result of it, any machine translation requires a thorough proof
reading and editing and this takes no less time and efforts and may be
as tiresome as the usual hand-made translation of the passage.

Literature used:

Weaver Warren – “Translation”. Cambridge, Mass.: Technology Press of
M.I.T., 1955.

Hutchins W.J. – “Machine Translation: Past, Present, Future”. “Wiley”,
Chichester, Ellis Horwood, N.Y. etc., 1986.

Materials from Machine Translation Summit VII, 13th-17th September 1999,
Kent Ridge Labs, Singapore.

“New Scientist Magazine” (www.newscientist.com):

“Device translates spoken Japanese and English” – 07/10/2004

“I think it thinks” – 06/10/2001

“Technology: Machine minds your language” – 26/10/1996

Беляева Л.Н., Откупщикова М.И. – “Прикладное языкознание” (Раздел –
Автоматический (машинный) перевод). Изд-во Санкт-Петербургского ун-та,
СПб., 2001.

Журнал “Вопросы языкознания” – Шаляпина З.М. – “Автоматический перевод:
эволюция и современные тенденции”, 1996, № 2.

Баранов А.Н. – “Введение в прикладную лингвистику” (Раздел – Машинный
перевод). УРСС, М., 2001.

Леонтьева Н.Н. – “К теории автоматического понимания естественных
текстов”. Издательство Московского университета, М., 2000.

Бакулов А.Д., Леонтьева Н.Н. – “Теоретические аспекты машинного
перевода”. Радио и связь, М., 1990.

Нелюбин Л.Л. – “Компьютерная лингвистика и машинный перевод”. ВЦП, М.,


Список литературы “для галочки”!!!

Реальный источник – HYPERLINK

Сдавалось Авдеенко В.П. – Киев, Май 2005.



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