Localizing Webpages for Francophone Audiences with Machine Translation

Localization is the process of translating the text and adapting the visual content of a software product for a specific target region. Given the global nature of the economy, organizations can appeal to nonEnglish speaking consumers by offering localized versions of their software or web application. Currently, the localization process calls for time-consuming and costly translations of text strings by language service providers (LSP) with each additional locale incurring a new cost for LSP’s manual translations. The high cost in terms of both time and money makes localizing software intimidating for organizations. This project explores the automation of the translation aspect of the localization process by using machine translation (MT) services. A Python script was developed to scrape HTML data and translate strings to French using Google Translate. The automatically generated translations were scored by human evaluators and the automated metric, BLEU. Participants were surprised at the quality of the translations, which were preserved meaning and were understandable despite some grammatical errors. This research suggests that the translation aspect of localizing software can be at least partially automated then edited for quality and fluency.