Improving machine translation literacy to facilitate and enhance scholarly communication

1. Abstract

English is the main language of scholarly communication, but, most researchers are not native English speakers. Contemporary machine translation approaches such as neural machine translation (NMT) are data-driven and use artificial-intelligence-based machine learning techniques; however, such tools rarely produce high quality output of specialized text without human intervention. There is an emerging need for machine translation (MT) literacy among non-Anglpohone students and faculty who must both read and write in English in order to participate fully in the scholarly communication process. We designed and pilot tested a machine translation literacy workshop to help researchers use MT more effectively for scholarly tasks such as: 1) search and discovery of scholarly texts; 2) reading and evaluating scholarly texts; 3) research communication in international teams; and 4) writing for scholarly publishing. Pre- and post-workshop surveys were used to evaluate the success of the workshop and recommend improvements for future iterations.

Lynne Bowker (, University of Ottawa, Canada

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