Évaluation de la production de quatre systèmes traduction automatique

Abstract

This thesis aims to contribute to the improvement of online machine translation software. We identify errors in the process of translation between English and French and make recommendations. The systems evaluated are Promt, Babylon, Google Translate and Bing and the reference corpus is taken from BankGloss. Promt made the most errors, followed by Babylon, Bing and Google. The systems together produced a total of 147 grammatical errors, 74 semantic errors, 17 lexical errors, and 6 stylistic errors. To improve Promt, we suggest expanding its dictionary. For Babylon, we advise adding more grammar rules. In order to reduce the number of semantic errors in Bing and Google, the software should learn to identify words according to context. Machine translation is not an end in itself, but a good aid in accomplishing translation tasks.