{"title":"When French becomes Canadian French","authors":"Lynne Bowker, F. Blain","doi":"10.1075/jial.22007.bow","DOIUrl":null,"url":null,"abstract":"\n In late 2020, the free online translation tool Microsoft Translator began to offer the option of translating into\n “French (Canada)” as a target language, alongside the previously offered “French”. Using a list of ten COVID-19 terms previously\n identified by Bowker (2020) as having different equivalents in Canadian French and\n European French, we evaluate the ability of Microsoft Translator to localize these terms into the two varieties of French. The\n findings indicate that while this tool does a good job of localizing the terms into Canadian French, it also uses a high number of\n Canadian French terms when the target language is set to “French”. One potential reason for this may be that the corpus used to\n train the tool for “French” contains a disproportionate number of examples from Canadian sources, and so there may be a problem of\n bias where the tool is amplifying Canadian French in the machine translation output.","PeriodicalId":36199,"journal":{"name":"Journal of Internationalization and Localization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Internationalization and Localization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1075/jial.22007.bow","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 2
Abstract
In late 2020, the free online translation tool Microsoft Translator began to offer the option of translating into
“French (Canada)” as a target language, alongside the previously offered “French”. Using a list of ten COVID-19 terms previously
identified by Bowker (2020) as having different equivalents in Canadian French and
European French, we evaluate the ability of Microsoft Translator to localize these terms into the two varieties of French. The
findings indicate that while this tool does a good job of localizing the terms into Canadian French, it also uses a high number of
Canadian French terms when the target language is set to “French”. One potential reason for this may be that the corpus used to
train the tool for “French” contains a disproportionate number of examples from Canadian sources, and so there may be a problem of
bias where the tool is amplifying Canadian French in the machine translation output.