Machine translation resurfaced as a viable business solution about 5 years ago, with much hype. With the amount of content requiring translation, and a mellowing of user expectations about translation quality, it seemed there was real business value in developing machine translation solutions. Since then, however, the discounts offered to enterprise customers have remained stubbornly meager in the 10-20% range, with high, up-front costs—far from the anticipated savings. This paper provides an overview of the challenges encountered in the value chain between customer and Language Service Provider (LSP) which keep translation costs high and limit machine translation adoption, discusses existing and potential solutions to these challenges, and offers suggestions on how to enlist the support of the LSP and freelance translator community to address these challenges.

Although still in a nascent state as a professional translation tool, customized SMT engines already have multiple applications, each of which require clear definitions about quality and productivity. Three engine-training scenarios have emerged which are representative of real-world applications for the development and use of a customized SMT engines based on the availability of data. In the case that limited or no bilingual training data is available, a unique development process can be used to harvest and translate n-grams directly. Using this approach Asia Online and Moravia IT have successfully customized SMT engines for use in various domains. A partnership between an MT engine provider and a qualified LSP is essential to deliver quality results using this approach.