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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 25

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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 percenta 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 26

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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 CECAFrench, 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~: 27

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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. 28