= [Quote]
Tim McKenna is an author who thinks and writes about the complexity of
truth in a world of flux. He wrote in October 2000: "When software gets
good enough for people to chat or talk on the web in real time in
different languages, then we will see a whole new world appear before
us. Scientists, political activists, businesses and many more groups
will be able to communicate immediately without having to go through
mediators or translators."
= A definition
Machine translation can be defined as the automated process of
translating a text from one language to another language. MT analyzes
the text in the source language and automatically generates the
corresponding text in the target language. With the lack of any human
intervention during the translation process, machine translation (MT)
differs from computer-assisted translation (CAT), which involves some
interaction between the translator and the computer.
As explained on the website of SYSTRAN, a company specializing in
translation software, "machine translation software translates one
natural language into another natural language. MT takes into account
the grammatical structure of each language and uses rules to transfer
the grammatical structure of the source language (text to be
translated) into the target language (translated text). MT cannot
replace a human translator, nor is it intended to."
The website of the European Association for Machine Translation (EAMT)
gives the following definition: "Machine translation (MT) is the
application of computers to the task of translating texts from one
natural language to another. One of the very earliest pursuits in
computer science, MT has proved to be an elusive goal, but today a
number of systems are available which produce output which, if not
perfect, is of sufficient quality to be useful for certain specific
applications, usually in the domain of technical documentation. In
addition, translation software packages which are designed primarily to
assist the human translator in the production of translations are
enjoying increasing popularity within professional translation
organizations."
Machine translation is the earliest type of natural language
processing, as stated on the website of Globalink, a company offering
language translation software and services: "From the very beginning,
machine translation (MT) and natural language processing (NLP) have
gone hand-in-hand with the evolution of modern computational
technology. The development of the first general-purpose programmable
computers during World War II was driven and accelerated by Allied
cryptographic efforts to crack the German Enigma machine and other
wartime codes. Following the war, the translation and analysis of
natural language text provided a testbed for the newly emerging field
of Information Theory.
During the 1950s, research on Automatic Translation (known today as
Machine Translation, or 'MT') took form in the sense of literal
translation, more commonly known as word-for-word translations, without
the use of any linguistic rules. The Russian project initiated at
Georgetown University in the early 1950s represented the first
systematic attempt to create a demonstrable machine translation system.
Throughout the decade and into the 1960s, a number of similar
university and government-funded research efforts took place in the
United States and Europe. At the same time, rapid developments in the
field of Theoretical Linguistics, culminating in the publication of
Noam Chomsky's "Aspects of the Theory of Syntax" (1965), revolutionized
the framework for the discussion and understanding of the phonology,
morphology, syntax and semantics of human language.
In 1966, the U.S. government-issued ALPAC (Automatic Language
Processing Advisory Committee) report offered a prematurely negative
assessment of the value and prospects of practical machine translation
systems, effectively putting an end to funding and experimentation in
the field for the next decade. It was not until the late 1970s, with
the growth of computing and language technology, that serious efforts
began once again. This period of renewed interest also saw the
development of the Transfer model of machine translation and the
emergence of the first commercial MT systems. While commercial ventures
such as SYSTRAN and METAL began to demonstrate the viability, utility
and demand for machine translation, these mainframe-bound systems also
illustrated many of the problems in bringing MT products and services
to market. High development cost, labor-intensive lexicography and
linguistic implementation, slow progress in developing new language
pairs, inaccessibility to the average user, and inability to scale
easily to new platforms are all characteristics of these second-
generation systems."
As explained in August 1998 by Eduard Hovy, head of the Natural
Language Group at USC/ISI (University of Southern
California/Information Sciences Institute), machine translation implies
"language-related applications/functionalities that are not
translation, such as information retrieval (IR) and automated text
summarization (SUM). You would not be able to find anything on the Web
without IR! -- all the search engines (AltaVista, Yahoo!, etc.) are
built upon IR technology. Similarly, though much newer, it is likely
that many people will soon be using automated summarizers to condense
(or at least, to extract the major contents of) single (long) documents
or lots of (any length) ones together."
= Experiences
In December 1997, AltaVista, a leading search engine, was the first to
launch a free translation software with Babel Fish -- also called
AltaVista Translation --, which could translate webpages (up to three
pages at the same time) from English into French, German, Italian,
Portuguese or Spanish, and vice versa. The software was developed by
SYSTRAN (an acronym for System Translation), a company specializing in
machine translation software. SYSTRAN's headquarters are located in
Soisy-sous-Montmorency, near Paris, France. Sales, marketing, and
research and development are based in its subsidiary in La Jolla,
California.
This initiative was followed by other translation software developed by
Alis Technologies, Globalink, Lernout & Hauspie, and Softissimo, with
free and/or paid versions on the web.
Based in Montreal, Quebec, Alis Technologies has specialized in
development and marketing of language handling solutions and services,
particularly language implementation in the information technology
industry. Alis Translation Solutions (ATS) has offered applications in
a number of languages, and tools and services to improve the quality of
translations. Language Technology Solutions (LTS) has marketed advanced
tools and services for language engineering and information technology
(90 languages covered).
Based in Ieper, Belgium, and Burlington, Massachusetts, Lernout &
Hauspie (L&H) was a leader in advanced speech technology for commercial
applications and products, with four core technologies: automatic
speech recognition (ASR), text-to-speech (TTS), text-to-text (TTT), and
digital speech compression (DSC). Its ASR, TTS and DSC technologies
were licensed to companies in telecommunications, computers and
multimedia, consumer electronics and automotive electronics. Its TTT
translation services were provided to IT companies, and vertical and
automation markets. The Machine Translation Group created by Lernout &
Hauspie included L&H Language Technology, AppTek, AILogic, NeocorTech,
and Globalink. Lernout & Hauspie was later bought by Nuance
Communications.
Globalink, a company created in 1990 in the U.S., focused on language
translation software and services, i.e. customized translation
solutions built around software products, online options, and
professional translation services. The software products were available
in Spanish, French, Portuguese, German, Italian and English, for
individuals, small businesses, multinational corporations and
governments, from a stand-alone product giving a fast draft translation
to a full system managing professional translations.
As explained on the company website in 1998, "with Globalink's
translation applications, the computer uses three sets of data: the
input text, the translation program and permanent knowledge sources
(containing a dictionary of words and phrases of the source language),
and information about the concepts evoked by the dictionary and rules
for sentence development. These rules are in the form of linguistic
rules for syntax and grammar, and some are algorithms governing verb
conjugation, syntax adjustment, gender and number agreement and word
re-ordering. Once the user has selected the text and set the machine
translation process in motion, the program begins to match words of the
input text with those stored in its dictionary. Once a match is found,
the application brings up a complete record that includes information
on possible meanings of the word and its contextual relationship to
other words that occur in the same sentence. The time required for the
translation depends on the length of the text. A three-page, 750-word
document takes about three minutes to render a first draft
translation."
At the headquarters of the World Health Organization (WHO) in Geneva,
Switzerland, the Computer-assisted Translation and Terminology Unit
(CTT) has been a pioneer since 1997 in assessing technical options for
using computer-assisted translation (CAT) systems based on translation
memory (TM). With such systems, translators can access previous
translations from portions of the text; accept, reject or modify them;
and add the new translation to the memory, thus enriching it for future
reference. By archiving the daily output, the translator helps in
building an extensive translation memory and in solving a number of
translation issues. Several projects have been under way at the CTT for
electronic document archiving and retrieval, bilingual/multilingual
text alignment, computer-assisted translation, translation memory and
terminology database management, and speech recognition.
The Pan American Health Organization (PAHO) in Washington, D.C. has
developed its own machine translation software, as a common work from
its own computational linguists, translators, and system programmers.
The PAHO Translation Unit has used SPANAM (Spanish to English) from
1980 and ENGSPAN (English to Spanish) from 1985, to process over 25
million words between 1980 and 1998. Staff translators and free-lance
translators post-edit the raw output to produce high-quality
translations with a 30-50% gain in productivity. The software is
available in the LAN (Local Area Network) of PAHO Headquarters, and is
regularly used by the staff of technical and administrative units. The
software is also available in a number of PAHO field offices, and has
been licensed to public and non-profit institutions in the U.S., Latin
America, and Spain. The software was later renamed PAHOMTS, and has
included new language pairs with Portuguese.
= Comments
# Comments from ZDNN
In "Web Embraces Language Translation", an article published in ZDNN
(ZDNetwork News) on 21 July 1998, Martha Stone explained: "Among the
new products in the $10 billion language translation business are
instant translators for websites, chat rooms, email and corporate
intranets. The leading translation firms are mobilizing to seize the
opportunities. Such as:
*SYSTRAN has partnered with AltaVista and reports between 500,000 and
600,000 visitors a day on babelfish.altavista.digital.com, and about 1
million translations per day -- ranging from recipes to complete
webpages. About 15,000 sites link to babelfish, which can translate to
and from French, Italian, German, Spanish and Portuguese. The site
plans to add Japanese soon. 'The popularity is simple. With the
internet, now there is a way to use U.S. content. All of these
contribute to this increasing demand,' said Dimitros Sabatakakis, group
CEO of SYSTRAN, speaking from his Paris home.
*Alis technology powers the Los Angeles Times' soon-to-be launched
language translation feature on its site. Translations will be
available in Spanish and French, and eventually, Japanese. At the click
of a mouse, an entire webpage can be translated into the desired
language.
*Globalink offers a variety of software and web translation
possibilities, including a free email service and software to enable
text in chat rooms to be translated.
But while these so-called 'machine' translations are gaining worldwide
popularity, company execs admit they're not for every situation.
Representatives from Globalink, Alis and SYSTRAN use such phrases as
'not perfect' and 'approximate' when describing the quality of
translations, with the caveat that sentences submitted for translation
should be simple, grammatically accurate and idiom-free. 'The progress
on machine translation is moving at Moore's Law -- every 18 months it's
twice as good,' said Vin Crosbie, a web industry analyst in Greenwich,
Conn. 'It's not perfect, but some [non-English speaking] people don't
realize I'm using translation software.'
With these translations, syntax and word usage suffer, because
dictionary-driven databases can't decipher between homonyms -- for
example, 'light' (as in the sun or light bulb) and 'light' (the
opposite of heavy). Still, human translation would cost between $50 and
$60 per webpage, or about 20 cents per word, SYSTRAN's Sabatakakis
said. While this may be appropriate for static 'corporate information'
pages, the machine translations are free on the web, and often less
than $100 for software, depending on the number of translated languages
and special features."
# Comments from RALI
Despite the imminent outbreak of a universal translation machine
announced at the end of the 1940s, machine translation hasn't produced
good translations yet. Pierre Isabelle and Patrick Andries, two
scientists from the RALI Laboratory (Laboratory for Applied Research in
Computational Linguistics - Laboratoire de Recherche Appliquée en
Linguistique Informatique) in Montreal, Quebec, explain the reasons for
this failure in "La Traduction Automatique, 50 Ans Après" (Machine
Translation, 50 Years Later), an article published in 1998 by
Multimédium, a French-language online magazine: "The ultimate goal of
building a machine capable of competing with a human translator remains
elusive due to slow progress in research. (...) Recent research, based
on large collections of texts called corpora -- using either
statistical or analogical methods -- has promised to reduce the
quantity of manual work required to build a machine translation (MT)
system, but can't promise for sure a significant improvement in the
quality of machine translation. (...) The use of MT will be more or
less restricted to tasks of information assimilation or tasks of text
distribution in restricted sub-languages."
According to Yehochua Bar-Hillel's ideas expressed in "The State of
Machine Translation", an article published in 1951, Pierre Isabelle and
Patrick Andries define three implementation strategies for machine
translation: (a) a tool of information assimilation to scan
multilingual data and supply rough translation, (b) situations of
"restricted language" such as the METEO system which, since 1977, has
translated the weather forecasts of the Canadian Ministry of
Environment, (c) the human/machine coupling before, during and after
the machine translation process, that may not save money if compared to
traditional translation.
Pierre Isabelle and Patrick Andries favor "a workstation for the human
translator" more than a "robot translator": "Recent research on the
probabilist methods showed it was possible to modelize in an efficient
way some simple aspects of the translation relationship between two
texts. For example, methods were set up to calculate the correct
alignment between the text sentences and their translation, that is, to
identify the sentence(s) of the source text corresponding to each
sentence of the translation. Applied on a large scale, these techniques
can use the archives of a translation service to build a translation
memory for recycling fragments from previous translations. Such systems
are already available on the translation market (IBM Translation
Manager II, Trados Translator's Workbench by Trados, RALI TransSearch,
etc.) The latest research focuses on models that can automatically set
up correspondences at a finer level than the sentence level, i.e.
syntagms and words. The results let hope for a bunch of new tools for
the human translator, including for the study of terminology, for
dictation and translation typing, and for detectors of translation
errors."
# Comments from Randy Hobler
In September 1998, Randy Hobler was a consultant in internet marketing
at Globalink, after working for IBM, Johnson & Johnson, Burroughs
Wellcome, Pepsi, and Heublein. He wrote in an email interview: "We are
rapidly reaching the point where highly accurate machine translation of
text and speech will be so common as to be embedded in computer
platforms, and even in chips in various ways. At that point, and as the
growth of the web slows, the accuracy of language translation hits 98%
plus, and the saturation of language pairs has covered the vast
majority of the market, language transparency (any-language-to-any-
language communication) will be too limiting a vision for those selling
this technology. The next development will be 'transcultural,
transnational transparency', in which other aspects of human
communication, commerce and transactions beyond language alone will
come into play. For example, gesture has meaning, facial movement has
meaning and this varies among societies. The thumb-index finger circle
means 'OK' in the United States. In Argentina, it is an obscene
gesture.
When the inevitable growth of multimedia, multilingual
videoconferencing comes about, it will be necessary to 'visually edit'
gestures on the fly. The MIT (Massachusetts Institute of Technology)
Media Lab, Microsoft and many others are working on computer
recognition of facial expressions, biometric access identification via
the face, etc. It won't be any good for a U.S. business person to be
making a great point in a web-based multilingual video conference to an
Argentinian, having his words translated into perfect Argentinian
Spanish if he makes the 'O' gesture at the same time. Computers can
intercept this kind of thing and edit them on the fly.
There are thousands of ways in which cultures and countries differ, and
most of these are computerizable to change as one goes from one culture
to the other. They include laws, customs, business practices, ethics,
currency conversions, clothing size differences, metric versus English
system differences, etc. Enterprising companies will be capturing and
programming these differences and selling products and services to help
the peoples of the world communicate better. Once this kind of thing is
widespread, it will truly contribute to international understanding."
= Machine translation R&D
Here is an overview of the work of four research centers, in Quebec
(RALI Laboratory), California (Natural Language Group), Switzerland
(ISSCO) and Japan (UNDL Foundation).
# RALI Laboratory
In Montreal, Quebec, the RALI Laboratory (Laboratory of Applied
Research in Computational Linguistics - Laboratoire de Recherche
Appliquée en Linguistique Informatique) has worked in automatic text
alignment, automatic text generation, automatic reaccentuation,
language identification, and finite state transducers. RALI produces
the "TransX family" of what it calls "a new generation" of translation
support tools (TransType, TransTalk, TransCheck, and TransSearch),
which are based on probabilistic translation models that automatically
calculate correspondences between the text produced by a translator and
the original text from the source language.
As explained on RALI's website in 1998: "(a) TransType speeds up the
keying-in of a translation by anticipating a translator's choices and
criticizing them when appropriate. In proposing its suggestions,
TransType takes into account both the source text and the partial
translation that the translator has already produced. (b) TransTalk is
an automatic dictation system that makes use of a probabilistic
translation model in order to improve the performance of its voice
recognition model. (c) TransCheck automatically detects certain types
of translation errors by verifying that the correspondences between the
segments of a draft and the segments of the source text respect well-
known properties of a good translation. (d) TransSearch allows
translators to search databases of pre-existing translations in order
to find ready-made solutions to all sorts of translation problems. In
order to produce the required databases, the translations and the
source language texts must first be aligned."
# Natural Language Group
The Natural Language Group (NLG) at the Information Sciences Institute
(ISI) of the University of Southern California (USC) has been involved
in various aspects of computational/natural language processing:
machine translation, automated text summarization, multilingual verb
access and text management, development of large concept taxonomies
(ontologies), discourse and text generation, construction of large
lexicons for various languages, and multimedia communication.
Eduard Hovy, head of the Natural Language Group, explained in August
1998: "People will write their own language for several reasons --
convenience, secrecy, and local applicability -- but that does not mean
that other people are not interested in reading what they have to say!
This is especially true for companies involved in technology watch
(say, a computer company that wants to know, daily, all the Japanese
newspaper and other articles that pertain to what they make) or some
Government Intelligence agencies (the people who provide the most up-
to-date information for use by your government officials in making
policy, etc.). One of the main problems faced by these kinds of people
is the flood of information, so they tend to hire 'weak' bilinguals who
can rapidly scan incoming text and throw out what is not relevant,
giving the relevant stuff to professional translators. Obviously, a
combination of SUM (automated text summarization) and MT (machine
translation) will help here; since MT is slow, it helps if you can do
SUM in the foreign language, and then just do a quick and dirty MT on
the result, allowing either a human or an automated IR-based text
classifier to decide whether to keep or reject the article. For these
kinds of reasons, the U.S. Government has over the past five years been
funding research in MT, SUM, and IR (information retrieval), and is
interested in starting a new program of research in Multilingual IR.
This way you will be able to one day open Netscape or Explorer or the
like, type in your query in (say) English, and have the engine return
texts in *all* the languages of the world. You will have them clustered
by subarea, summarized by cluster, and the foreign summaries
translated, all the kinds of things that you would like to have."
Eduard Hovy added in August 1999: "Over the past 12 months I have been
contacted by a surprising number of new information technology (IT)
companies and startups. Most of them plan to offer some variant of
electronic commerce (online shopping, bartering, information gathering,
etc.). Given the rather poor performance of current non-research level
natural language processing technology (when is the last time you
actually easily and accurately found a correct answer to a question to
the web, without having to spend too much time sifting through
irrelevant information?), this is a bit surprising. But I think
everyone feels that the new developments in automated text
summarization, question analysis, and so on, are going to make a
significant difference. I hope so!--but the level of performance is not
available yet.
It seems to me that we will not get a big breakthrough, but we will get
a somewhat acceptable level of performance, and then see slow but sure
incremental improvement. The reason is that it is very hard to make
your computer really 'understand' what you mean -- this requires us to
build into the computer a network of 'concepts' and their
interrelationships that (at some level) mirror those in your own mind,
at least in the subjects areas of interest. The surface (word) level is
not adequate -- when you type in 'capital of Switzerland', current
systems have no way of knowing whether you mean 'capital city' or
'financial capital'. Yet the vast majority of people would choose the
former reading, based on phrasing and on knowledge about what kinds of
things one is likely to ask the web, and in what way. Several projects
are now building, or proposing to build, such large 'concept' networks.
This is not something one can do in two years, and not something that
has a correct result. We have to develop both the network and the
techniques for building it semi-automatically and self-adaptively. This
is a big challenge."
Eduard Hovy added in September 2000: "I see a continued increase in
small companies using language technology in one way or another: either
to provide search, or translation, or reports, or some other
communication function. The number of niches in which language
technology can be applied continues to surprise me: from stock reports
and updates to business-to-business communications to marketing...
With regard to research, the main breakthrough I see was led by a
colleague at ISI (I am proud to say), Kevin Knight. A team of
scientists and students last summer at Johns Hopkins University in
Maryland developed a faster and otherwise improved version of a method
originally developed (and kept proprietary) by IBM about 12 years ago.
This method allows one to create a machine translation (MT) system
automatically, as long as one gives it enough bilingual text.
Essentially the method finds all correspondences in words and word
positions across the two languages and then builds up large tables of
rules for what gets translated to what, and how it is phrased.
Although the output quality is still low -- no-one would consider this
a final product, and no-one would use the translated output as is --
the team built a (low-quality) Chinese-to-English MT system in 24
hours. That is a phenomenal feat -- this has never been done before.
(Of course, say the critics: you need something like 3 million sentence
pairs, which you can only get from the parliaments of Canada, Hong
Kong, or other bilingual countries; and of course, they say, the
quality is low. But the fact is that more bilingual and semi-equivalent
text is becoming available online every day, and the quality will keep
improving to at least the current levels of MT engines built by hand.
Of that I am certain.)
Other developments are less spectacular. There's a steady improvement
in the performance of systems that can decide whether an ambiguous word
such as "bat" means "flying mammal" or "sports tool" or "to hit"; there
is solid work on cross-language information retrieval (which you will
soon see in being able to find Chinese and French documents on the web
even though you type in English-only queries), and there is some rather
rapid development of systems that answer simple questions automatically
(rather like the popular web system AskJeeves, but this time done by
computers, not humans). These systems refer to a large collection of
text to find 'factiods' (not opinions or causes or chains of events) in
response to questions such as 'what is the capital of Uganda?' or 'how
old is President Clinton?' or 'who invented the xerox process?', and
they do so rather better than I had expected."
# ISSCO
In Geneva, Switzerland, ISSCO (Dalle Molle Institute for Semantic and
Cognitive Studies - Institut Dalle Molle pour les Études Sémantiques et
Cognitives) is a research laboratory conducting basic and applied
research in computational linguistics (CL) and artificial intelligence
(AI), for a number of Swiss and European research projects. The
University of Geneva has provided administrative support and
infrastructure. Research is funded with grants and contracts with
public and private bodies.
Created by the Foundation Dalle Molle in 1972 to conduct research in
cognition and semantics, ISSCO has come to specialize in natural
language processing, including multilingual language processing, in a
number of areas: machine translation, linguistic environments,
multilingual generation, discourse processing, data collection, etc.
ISSCO is multi-disciplinary and multi-national. As explained on its
website in 1998, "its staff and its visitors [are drawn] from the
disciplines of computer science, linguistics, mathematics, psychology
and philosophy. The long-term staff of the Institute is relatively
small in number; with a much larger number of visitors coming for stays
ranging from a month to two years. This ensures a continual exchange of
ideas and encourages flexibility of approach amongst those associated
with the Institute."
# UNDL Foundation
The UNL (universal networking language) project was launched in the
mid-1990s as a main digital metalanguage project by the Institute of
Advanced Studies (IAS) of the United Nations University (UNU) in Tokyo,
Japan. As explained on the bilingual (English, Japanese) website in
1998: "UNL is a language that -- with its companion 'enconverter' and
'deconverter' software -- enables communication among peoples of
differing native languages. It will reside, as a plug-in for popular
web browsers, on the internet, and will be compatible with standard
network servers. The technology will be shared among the member states
of the United Nations. Any person with access to the internet will be
able to 'enconvert' text from any native language of a member state
into UNL. Just as easily, any UNL text can be 'deconverted' from UNL
into native languages. United Nations University's UNL Center will work
with its partners to create and promote the UNL software, which will be
compatible with popular network servers and computing platforms."
In 2000, 120 researchers worldwide were working on a multilingual
project in 16 languages (Arabic, Brazilian, Chinese, English, French,
German, Hindu, Indonesian, Italian, Japanese, Latvian, Mongolian,
Russian, Spanish, Swahiki, and Thai). The UNDL Foundation (UNDL:
Universal Networking Digital Language) was founded in January 2001 to
develop and promote the UNL project.