This paper describes an ongoing project which has the goal of improving machine translation quality by increasing knowledge about the text to be translated. A basic piece of such knowledge is the domain or subject field of the text. When this is known, it is possible to improve meaning selection appropriate to that domain. Our current effort consists in automating both recognition of the text’s domain and the assignment of domain-specific translations. Results of our implementation show that the approach of using terminology categorization already existing in the machine translation system is very promising.

On December 9 1997, SYSTRAN and the AltaVista Search Network launched the first widely available, real-time, high-speed and free translation service on the Internet. This initial deployment, treated as a global experiment, has become a tremendous success. Through this service, machine translation (MT) technology has been pushed to the forefront of worldwide awareness. Besides growing media coverage, user response during the first five months has been overwhelming. This paper is a study of the user feedback from the MT developer’s perspective, addressing such questions as: Who are the users? What are their needs? What is their acceptance of MT? What types of texts are being translated? What suggestions do users offer? Finally, this paper outlines our view on opportunities and challenges, and on how to use this feedback to guide future development priorities.