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In this paper we argue that the time is ripe for translator educators to engage with Statistical Machine Translation (SMT) in more profound ways than they have done to date. We explain the basic principles of SMT and reflect on the role of humans in SMT workflows. Against a background of diverging opinions on the latter, we argue for a holistic approach to the integration of SMT into translator training programmes, one that empowers rather than marginalises translators. We discuss potential barriers to the use of SMT by translators generally and in translator training in particular, and propose some solutions to problems thus identified. More specifically, cloud-based services are proposed as a means of overcoming some of the technical and ethical challenges posed by more advanced uses of SMT in the classroom. Ultimately the paper aims to pave the way for the design and implementation of a new translator-oriented SMT syllabus at our own University and elsewhere.

This paper describes a large-scale survey of machine translation (MT) competencies conducted by a non-commercial and publicly funded European research project. Firstly, we highlight the increased prevalence of translation technologies in the translation and localisation industry, and develop upon this by reporting on survey data derived from 438 validated respondents, including freelance translators, language service providers, translator trainers, and academics. We then focus on ascertaining the prevalence of translation technology usage on a fine-grained scale to address aspects of MT, quality assessment techniques and post-editing. We report a strong need for an improvement in quality assessment methods, tools, and training, partly due to the large variance in approaches and combinations of methods, and to the lack of knowledge and resources. We note the growing uptake of MT and the perceived increase of its prevalence in future workflows. We find that this adoption of MT has led to significant changes in the human translation process, in which post-editing appears to be exclusively used for high-quality content publication. Lastly, we echo the needs of the translation industry and community in an attempt to provide a more comprehensive snapshot to inform the provision of translation training and the need for increased technical competencies.

The use of video has become well established in education, from traditional courses to blended and online courses. It has grown both in its diversity of applications as well as its content. Such educational video however is not fully accessible to all students, particularly those who require additional visual support or students studying in a foreign language. Subtitles (also known as captions) represent a unique solution to these language and accessibility barriers, however, the impact of subtitles on cognitive load in such a rich and complex multimodal environment has yet to be determined. Cognitive load is a complex construct and its measurement by means of single indirect and unidimensional methods is a severe methodological limitation. Building upon previous work from several disciplines, this paper moves to establish a multimodal methodology for the measurement of cognitive load in the presence of educational video. We show how this methodology, with refinement, can allow us to determine the effectiveness of subtitles as a learning support in educational contexts. This methodology will also make it possible to analyse the impact of other multimedia learning technology on cognitive load.

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This paper reports on the results of a project that aimed to investigate the usability of raw machine translated technical support documentation for a commercial online file storage service. Adopting a user-centred approach, we utilize the ISO/TR 16982 definition of usability -goal completion, satisfaction, effectiveness, and efficiency -and apply eye-tracking measures shown to be reliable indicators of cognitive effort, along with a post-task questionnaire. We investigated these measures for the original user documentation written in English and in four target languages: Spanish, French, German and Japanese, all of which were translated using a freely available online statistical machine translation engine. Using native speakers for each language, we found several significant differences between the source and MT output, a finding that indicates a difference in usability between well-formed content and raw 2 machine translated content. One target language in particular, Japanese, was found to have a considerably lower usability level when compared with the original English.

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Eye tracking has been used successfully as a technique for measuring cognitive load in reading, psycholinguistics, writing, language acquisition etc for some time now. Its application as a technique for automatically measuring the reading ease of MT output has not yet, to our knowledge, been tested. We report here on a preliminary study testing the use and validity of an eye tracking methodology as a means of semiand/or automatically evaluating machine translation output. 50 French machine translated sentences, 25 rated as excellent and 25 rated as poor in an earlier human evaluation, were selected. 10 native speakers of French were instructed to read the MT sentences for comprehensibility. Their eye gaze data were recorded noninvasively using a Tobii 1750 eye tracker. The average gaze time and fixation count were found to be higher for the "bad" sentences, while average fixation duration and pupil dilations were not found to be substantially different between output rated as good or bad. Comparisons between BLEU scores and eye gaze data were also made and found to correlate well with gaze time and fixation count, and to a lesser extent with pupil dilation and fixation duration. We conclude that the eye tracking data, in particular gaze time and fixation count, correlate reasonably well with human evaluation of MT output but fixation duration and pupil dilation may be less reliable indicators of reading difficulty for MT output. We also conclude that eye tracking has promise as an automatic MT Evaluation technique.