Call for Papers

Google Translate & Modern Languages Education

Centre for Translation and Comparative Cultural Studies
University of Nottingham

Google Translate (GT) has become an institution in machine translation that has been claimed by its provider to be developing at great pace to achieve ever higher degrees of accuracy (Wu et al 2016) and to be able to handle an ever-widening network of language pairings through the introduction of Google’s Neural Machine Translation System (Wong 2016). Because GT is freely available on the internet and has its own app on computers, tablets and smartphones, it is accessible anywhere the Internet and Google services are available, and it easily enables users to render stretches of one language into another with outcomes of varying quality and comprehensibility (cf Van Rensburg, Snyman & Lotz 2012; Groves & Mundt 2015).
Because this technology is so readily available and user-friendly, it can be quite safely assumed that people will use it when they encounter unfamiliar languages or languages they are in the process of acquiring or that they need for the purpose of their own ongoing education. As such, GT has, perhaps inadvertently, become a player in education at all levels. For instance, it would allow a beginning learner of French to translate a reading exercise into their first language to potentially facilitate comprehension. Equally, a native speaker of German studying at a British university might prefer writing their assignments, or parts thereof, in their first language and then rendering them into English with the aid of this technology.
Given the wide range of potential uses (to positive or adverse effect) GT needs to be considered in context of education from angles such as its actual current abilities, pedagogical implications, ethics, institutional policies and also from the perspective of teachers and students.
In this light, this conference is seeking papers that address the usefulness and the use of GT in the context of education. It aims to comprise a range of topics, possibly from, but not limited to the following areas:

• The benefits and drawbacks of GT for second language acquisition
• GT’s role in independent language learning
• GT in the classroom
• GT and assessed coursework assignments
• GT and cheating in assessed work
• GT as research facilitator and related issues (e.g. rigor, ethics)
• Google Pixel Buds and the future for human interpreters
• GT and careers options for modern languages students
• GT’s translation quality/reliability

Each contribution will consist of a 20-minute presentation and a 10-minute Q&A session. We seek contributions from all relevant areas (e.g. vocational, secondary, tertiary, adult education, lifelong learning, professional and non-professional translators) by educators, students, researchers and professionals alike.

Please submit abstracts of up to 250 words along with a short bio (up to 50 words) to: klaus.mundt@nottingham.ac.uk

Submission deadline: 31 January 2018
Date of the event: 29 June 2018

References:
Groves, M., Mundt, K. (2015) ‘Friend or foe? Google Translate in language for academic purposes.’ English for Specific Purposes 37: 112-121.
van Rensburg, A., Snyman, C., Lotz, S. (2012) ‘Applying Google Translate in a higher education environment: Translation products assessed.’ Southern African Linguistics and Applied Language Studies 30 (4): 511-524.
Wong, S. (2016) ‘Google Translate AI invents its own language to translate with.’ New Scientist [online] 30/11/2016 https://www.newscientist.com/article/2114748-google-translate-ai-invents-its-own-language-to-translate-with/ [7/10/2017]
Wu, Y., Schuster, M. Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, Ł., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W.,Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., Dean, J. (2016) ‘Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.’ eprint arXiv:1609.08144 <https://arxiv.org/abs/1609.08144v2>[7/10/2017]