In this paper, we present the Moses-based infrastructure we developed and use as a productivity tool for the localisation of software documentation and user interface (UI) strings at Autodesk into twelve languages. We describe the adjustments we have made to the machine translation (MT) training workflow to suit our needs and environment, our server environment and the MT Info Service that handles all translation requests and allows the integration of MT in our various localisation systems. We also present the results of our latest post-editing productivity test, where we measured the productivity gain for translators post-editing MT output versus translating from scratch. Our analysis of the data indicates the presence of a strong correlation between the amount of editing applied to the raw MT output by the translators and their productivity gain. In addition, within the last calendar year our system has processed over thirteen million tokens of documentation content of which we have a record of the performed post-editing. This has allowed us to evaluate the performance of our MT engines for the different languages across our product portfolio, as well as spotlight potential issues with MT in the localisation process.

With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator throughput, in particular focusing on the use of high-quality Statistical Machine Translation (SMT) supplementing the established Translation Memory (TM) technology. In this paper, we present a novel modular approach that utilises state-of-the-art sub-tree alignment and SMT techniques to turn the fuzzy matches from a TM into near-perfect translations. Rather than relegate SMT to a last-resort status where it is only used should the TM system fail to produce the desired output, for us SMT is an integral part of the translation process that we rely on to obtain high-quality results. We show that the presented system consistently produces better-quality output than the TM and performs on par or better than the standalone SMT system.