Applying Corpus Data to Define Needs in Web Localization Training

Abstract

La localisation prend de plus en plus d’importance dans les programmes universitaires d’enseignement de la traduction. Cependant, peu de recherches empiriques se penchent sur des questions telles que la relation entre localisation et traduction, les champs de compétence spécifiques à la localisation ou sur la manière de rendre compte des différences culturelles en rapport avec le genre des productions numériques, les types textuels et les conventions en usage. Se basant sur les recherches antérieures menées sur les compétences traductionnelles, le présent article vise à proposer les fondements de l’étude des compétences en localisation. La recherche a mis en oeuvre une étude empirique contrastive, fondée sur des corpus, des traductions d’étudiants, ainsi que des données provenant d’un corpus comparable, constitué de textes originaux en espagnol et de textes localisés en espagnol. Notre objectif est d’identifier les différences dans la production des textes numériques localisés par des étudiants et des professionnels, d’une part, et des textes originaux, de l’autre. Cette analyse contrastive vise à mieux comprendre comment les compétences en localisation sont en lien avec le concept inclusif de compétences traductionnelles, afin de relever les aspects sur lesquels devrait porter la formation en localisation au niveau universitaire.Localization is increasingly making its way into translation training programs at university level. However, there is still a scarce amount of empirical research addressing issues such as defining localization in relation to translation, what localization competence entails or how to best incorporate intercultural differences between digital genres, text types and conventions, among other aspects. In this paper, we propose a foundation for the study of localization competence based upon previous research on translation competence. This project was developed following an empirical corpus-based contrastive study of student translations (learner corpus), combined with data from a comparable corpus made up of an original Spanish corpus and a Spanish localized corpus. The objective of the study is to identify differences in production between digital texts localized by students and professionals on the one hand, and original texts on the other. This contrastive study allows us to gain insight into how localization competence interrelates with the superordinate concept of translation competence, thus shedding light on which aspects need to be addressed during localization training in university translation programs

Neural machine translation and the evolution of the localisation sector

“…For instance, Jiménez Crespo and Tercedor (2011) developed a model of localisation competence seen as subordinate to the broader translation competence. However, Pym (2014) questions the existence of a localisation competence since localisation processes traditionally involve the division of tasks among different people in industry, and what employers expect from localisers is a set of different skills.…”

Investigating students’ attitudes towards translation technology: The status quo and structural relations with translation mindsets and future work self

“…(3) In response, a substantial bulk of study has been done on TTT course design with detailed accounts of what to teach and how to teach. In terms of what to teach, scholars have conducted enormous research on its teaching contents and resources in various sub-topics, such as computer-assisted translation ( Doherty and Moorkens, 2013 ; Enríquez Raído, 2013 ; Shuttleworth, 2017 ; Rodríguez-Castro, 2018 ), term management ( Martínez and Benítez, 2009 ), localization ( Jiménez-Crespo and Tercedor, 2011 ; Jiménez-Crespo, 2013 ), statistical machine translation ( Doherty and Kenny, 2014 ), machine translation and post-editing ( Moorkens, 2018 ; Guerberof Arenas and Moorkens, 2019 ), technical writing ( Tao et al, 2020 ), specialized corpora ( Rodríguez-Inés, 2013 ), python-based repository ( Krüger, 2021 ), and data science ( Yan and Wang, 2022 ). Meanwhile, topics like the technological skill-sets that student translators are required to acquire were often discussed ( Austermühl, 2013 ; Gaspari et al, 2015 ; He and Tao, 2022 ).…”