Description of Moz, a translation support system designed for texts exhibiting a high proportion of structured and semi-structured terminological content. The system comprises a web-based collaborative translation memory, with high recall via subsentential linguistic analysis and facilities for messaging and quality assurance. It is in production use, translating some 140,000 words per week.

We describe an experiment in rapid development of a statistical machine translation (SMT) system from scratch, using limited resources: under this heading we include not only training data, but also computing power, linguistic knowledge, programming effort, and absolute time.

Although undeniably useful for the translation of certain types of repetitive document, current translation memory technology is limited by the rudimentary techniques employed for approximate matching. Such systems, moreover, incorporate no real notion of a document, since the databases that underlie them are essentially composed of isolated sentence strings. As a result, current TM products can only exploit a small portion of the knowledge residing in translators’ past production. This paper examines some of the changes that will have to be implemented if the technology is to be made more widely applicable.