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Machine-Aided Translation
at Mannheim ~
and Luxembourg
As it becomes increasingly evident that fully automatic high-quality
machine translation was not going to be realized for a long time,
interest began to be shown in machine-aided translation. The Com-
mittee has knowledge of two important machine-aided translation
systems in operation: the Federal Armed Forces Translation
Agency, Mannheim, Germany, and the Terminological Bureau of
the European Coal and Steel Community, Luxembourg. At these
centers the approach is conservative; a machine is used to produce
specialized glossaries helpful in the translation of particular docu-
ments. (Although the translation system in operation at the USAF
Foreign Technology Division, Wright-Patterson Air Force Base, is
being called, with increasing frequency, ',machine- aided translation,"
it is actually a system of human-aided machine translation, relying,
as it must, on posteditors to make up for the deficiencies of the
machine output.)
MACHINE-AIDED TRANSLATION AT THE
FEDERAL ARMED FORCES TRANSLATION AGENCY,
MANNHEIM, GERMANY
The Federal Armed Forces Translation Agency conducted an ex-
periment designed to determine to what extent and in what areas
machine output could aid the human translator. Two translators
were given identical English texts to be translated into German.
Neither translator was a specialist in the technical field treated
in the text. Translator A had the conventional dictionaries and
other reference works found in technical libraries and access to
experienced experts. Translator B was given only a text-based or
text-related glossary (TRG) that listed all and only the technical
terms in the original text in the sequence in which they occurred
plus their German equivalent or equivalents. To minimize any
differences in the translators' abilities, a second text was
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translated in which translator A used the TRG and translator
B worked in the conventional way.
The procedure above was repeated with two different translators
and two different technical texts. Results of the test indicated that
a translator working with conventional aids requires between 50-
86 percent (average, 66 percent) more time than a translator work-
ing with a text-related glossary. In addition to increased speed,
another advantage of the TRG type of translation was that using
this method the translators made one third fewer errors.
We quote below from a translation of a paper titled "Production
of Text-Related Technical Glossaries by Digital Computer, A Pro-
cedure to Provide an Automatic Translation Aid," by F. Krollmann,
H. J. Schuck, and U. Winkler (the German original appeared in the
January 1965 issue of Beitrage zur Sprachkunde und Informations-
verarbeitung):
These two experiments have shown that the speed (and thus the cost) of the
translator's work as well as the quality of his product (and thus the output
of the editor) can be considerably improved if it is possible to relieve the
translator of the unproductive and tiresome search for the correct techni-
cal term that frequently cannot possibly be included yet in any of the con-
ventional dictionaries. These figures would suggest that, ideally, the error
quota in translations of technical-scientific texts can be reduced by appro=-
mately 40 percent—a figure which experience indicates can be improved by
at least another 10-15 percent since better understanding of the text fre-
quently results in improved linguistic rendition (unambiguity of style)—and
that translator productivity can be increased by over 50 percent.
The system works in the following way. The translator reads
through the text to be translated and underlines the English words
for which he desires to know the German equivalent. The text is
then given to a keypunch operator who punches the cards for the
underlined words and at the same time performs morphological
reduction of the English words (in most cases this simply involves
omitting the inflectional suffixes). The information on the cards is
then put into the computer, which can produce three or four text-
related glossaries in about 10 min. The TRG system became opera-
tional in 1965 and in early 1966 was connected by a data-link with a
Telefunken TR-4 computer in Trier.
At present the Federal Air Force Translation Agency has a co-
operative agreement for exchange of terminologies with the U.S.
Defense Language Institute/West Coast Branch, the British
Admiralty, the European Coal and Steel Community, and others.
An analysis of a test run and some sample output is to be found
in Appendix 12. This technique was developed by the Federal
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Ministry of Defense of West Germany which very kindly made
available for the Committee use of the material in Appendix 12.
MACHI NE-AIDED TRANSLATION AT THE
EUROPEAN COAL AND STEEL COMMUNITY,
LUXEMBOURG
The Terminological Bureau of the European Coal and Steel Com-
munity (CECA) was established in 1950 to provide assistance to the
Translation Bureau, which had the task of performing translations
into and out of the four official languages of CECA—French, Dutch,
Italian, and German.
The Head of the Terminological Bureau, Mr. J. A. Bachrach,
estimates that a minimum of 25 percent of the translator's time is
spent on terminological questions and that, in difficult documents,
up to 75 percent of the translator's time is spent on these problems.
In collaboration with Mrs. Lydia Hirschberg of the Free University
of Brussels and her group, various approaches to this problem were
considered. Soon a system was devised by which the translator's
time-consuming job of finding the answers to questions of termi-
nology was made easier.
The system utilized at CECA is one of automatic dictionary
look-up with context included. The operation is similar to that
used at Mannheim, but the output is somewhat different. It is simi-
lar in that the translator indicates, by underlining, the words with
which he desires help. The entire sentence is then keypunched and
fed into a computer. The computer goes through a search routine
and prints out the sentence or sentences that most nearly match (in
lexical items) the sentences in question. The translator then re-
ceives the desired items printed out with their context and in the
order in which they occur in the source.
The translation of the sentence is not done by the computer, but
by a human translator. However, since the data produced by each
query are added to the data base, the more the system is in use, the
greater is the probability of finding sentences that have the desired
term in the proper context. A sample of typical CECA French-
English output in shown in Appendix 13.
The information that has been built up by CECA not only is of
value in answering the queries of translators but also enables
CECA to publish specialized glossaries in a very short time.
Appendix 13, a copy of one extract from a five-language glossary
prepared for the Congress on Steel Utilization is attached.
The Committee finds it difficult to assess the difficulty and cost
of postediting. An initial reaction is apt to be like that of R. T. Beyer
Whys. Today_ (1), 50 (1965~:
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I must confess that the results were most unhappy. I found that I spent at
least as much time In editing as if I had carried out the entire translation
from the start. Even at that, I doubt if the edited translation reads as
smoothly as one which I would have started from scratch. I drew the con-
clusion that the machine today translates from a foreign language to a form
of broken English somewhat comparable to pidgin English. But it then re-
mains for the reader to learn this patois in order to understand what the
Russian actually wrote. Learning Russian would not be much more difficult.
Someday, perhaps, the machines will make it, but I as a translator do not
yet believe that I must throw my monkey wrench into the machinery in order
to prevent my technological unemployment.
The Committee had some postediting done as an experiment (see
Appendix 14~. Postediting took as long as translation, yet people
said they were willing to do it for less per word! FTD figures
indicate that in-house postediting is done faster than in-house
translation.
Studies of the FTD operation indicate that keyboard transcrip-
tion of the cyrillic text is a very minor part of the total cost. Thus,
automatic character recognition could cut the cost of the operation
only a little. On the other hand, a large fraction of the cost is in
putting the final translation together, with figures and equations,
and reproducing it.
If we compare the cost of human in-house translation ($40 per
1,000 Russian words) with the cost of machine-aided translation
within FTD ($36 per 1,000 Russian words), machine-aided transla-
tion appears to be somewhat less expensive. But FTD machine-
aided translation is costlier than contract translation ($33 per 1,000)
and far costlier than Joint Publications Research Service (JPRS)
translation ($16 per 1,000 English words).
Appendix 15 gives data on a comparison by experts of the quality
of some recent JPRS translations and FTD machine-aided trans-
lations. The text of the JPRS translations was judged to be better
than that of the FTD translations. The quality of the reproduction
of text and figures was judged to be poor in both cases, with JPRS
superior to FTD. We wonder why the Air Force pays more for
translations made by FTI) than superior and prompter JPRS
translations would cost.
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Representative terms from entire chapter:
translation agency
