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