Statistical machine translation in the translation curriculum: overcoming obstacles and empowering translators

Abstract

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.

Should Math Tools and Quantitative Methods be Part of University-based Translator and Interpreter’s Training? Russian Graduates’ Voices in the Focus

“…Nonetheless, the course on MTQM requires from its designers and teachers to focus their efforts on the verbal and cognitive aspects of the course delivery. The importance of adequate teaching discourse has been mentioned in previous papers regarding humanities (Fedotova & Chigisheva, 2015;Masalimova et al, 2017) as scholars mention that students who study on language programs should be explained the issues regarding technology, IT, and science topics in a plain language (Kenny & Doherty 2014). The present research findings within a new research context also argue for a consistent practice skills-oriented material to be offered to students in a plain language through the module under study.…”

“…When trying to explore the reasons for the above situation its seems necessary to take into account the Academia and Industry practice and opinions on math tools and quantitative methods (further MTQM) as part of university-translators' training as there is often a gap between academic contents and industry requirements regarding T&I specialists' competences and skills (Kenny & Doherty, 2014). Thus, the questions related to the course on MTQM content within academic curriculum, the course concept as viewed by various stakeholders, including employers, teachers, students, deserve researchers' attention.…”

Technology, translation and society

“…On the other hand, translators also seem likely to continue to be commissioned to translate content that is described as 'creative' or that plays a significant function in external relations while also being expected to manage and understand MT systems, post-editing processes and techniques. They may also have involvement in MT engine training, tuning or evaluation (Kenny and Doherty 2014). Thus, these skills are added to the existing competence requirements in linguistic, cultural, subject-specific, professional, interpersonal and technical domains.…”

“…However, translation technologies are one area that has remained untouched by SCOT. Some recent contributions by translation scholars (Olohan 2011, Kenny 2011, Cronin 2013, Kenny and Doherty 2014, O'Hagan 2016, Littau 2016) mark the beginnings of critical studies of translation technology and materiality, but there is much untapped SCOT-inspired potential, whether we focus on material objects or artefacts (for example a piece of translation memory software), a practice (for example post-editing machine translation), or knowledge (for example the knowing enacted in the practice of using a translation project management system).…”

Introducing statistical machine translation in translator training: from uses and perceptions to course design, and back again

“…In explaining statistical processing, we stressed that the data used were nothing more than a compilation of all existing translations made by DGT translators (see Way and Hearne, 2011, on the importance of disseminating such knowledge among translators), and we introduced students to EURAMIS, the European Advanced Multilingual Information System containing the DGT's TM and connecting it to the current translation environment through a metasearch engine called Quest. To explain language models and probabilistic treatment within SMT, we had students compute simple probabilities from a very small corpus, as explained in Kenny and Doherty (2014: 278 et sq; see also Hearne and Way, 2011 for a more in-depth presentation)…”