Categorias de uso de sistemas de tradução automática no contexto acadêmico.

Abstract

The use of machine translation systems has been increasingly recurrent in the commercial field since the late 1990s (CASTELL, 1999; HUTCHINS, 2000). The adoption of these systems in the academic and domicile sphere has also become more frequent soon after the arrival of digital societies, achieving more impetus in the 21st century because of the technological advance and the internet boom (SMITH, 2001; WEININGER 2004; KOHEN 2010, KUHN, 2013, SANTOS 2017, GOMES, 2018). Taking into account such an increase in machine translation users, a German researcher called Kohen (2010) classified the uses of these systems into three categories: a) assimilation, b) communication and c) publication. Currently, these categories are the ones to rule wherever and whenever machine translation systems are in use (SANTOS, 2014). In view of such a categorization, as well as the increasing demand of users of these systems, this study aimed at investigating the most frequent categories of machine translation use in the academic sphere at UFCG, followed by the systems in use in such a context and the kind of users of those systems. For that purpose, two groups of machine translation potential users in the academic context answered two research questionnaires: teachers and students. Regarding the results, both groups of respondents chose Google Translate as the most used system in their academic daily situations, among other systems such as systran, bing, linguee, babylon, translator and voice translator. On the one hand, text assimilation from English into Portuguese revealed to be the most frequent use category among the group of students. On the other hand, among teachers publication was the most frequent one. In that specific case, publication took place as a means for planning and documentation of material used along classes. Although some of the respondents mentioned the communication category, it was not so recurrent among the analyzed data.