Despite the growth of statistical machine translation (SMT) research and development in recent years, it remains somewhat out of reach for the translation community where programming expertise and knowledge of statistics tend not to be commonplace. While the concept of SMT is relatively straightforward, its implementation in functioning systems remains difficult for most, regardless of expertise. More recently, however, developments such as SmartMATE have emerged which aim to assist users in creating their own customized SMT systems and thus reduce the learning curve associated with SMT. In addition to commercial uses, translator training stands to benefit from such increased levels of inclusion and access to state-of-the-art approaches to MT. In this paper we draw on experience in developing and evaluating a new syllabus in SMT for a cohort of post-graduate student translators: we identify several issues encountered in the introduction of student translators to SMT, and report on data derived from repeated measures questionnaires that aim to capture data on students’ self-efficacy in the use of SMT. Overall, results show that participants report significant increases in their levels of confidence and knowledge of MT in general, and of SMT in particular. Additional benefits – such as increased technical competence and confidence – and future refinements are also discussed.

This paper reports on a project whose aims are to investigate the usability of raw machine translated technical support documentation for a commercial online file storage service. Following the ISO/TR 16982 definition of usability - goal completion, satisfaction, effectiveness, and efficiency - comparisons are drawn for all measures between the original user documentation written in English for a well-known online file storage service and raw machine translated output in four target languages: Spanish, French, German and Japanese. Using native speakers for each language, we found significant differences between the source and MT output for three out of the four measures: goal completion, efficiency and user satisfaction. This leads to a tentative conclusion that there is a difference in usability between well-formed content and raw machine translated content, and we suggest avenues for further work.