Traduction automatique, memoire de traduction ou traduction humaine? Proposition d'une approche pour determiner la meilleure methode a adopter, selon le texte (French text)

Over the past several years, there has been such an enormous increase in the demand for translation services that translators have been struggling to keep up with it. This situation makes the use of computer translation tools ever more attractive. But once these programs have been installed, how can translators decide which one to use? Or whether it is worth the trouble of using them at all? The objective of this thesis is to answer these questions by proposing and testing an approach to predict which translation method---machine translation, translation memories or human translation---will be the best for a given text type. In this case "best" means the method that allows a translator to produce a "client-ready" translation as quickly as possible.; After a review of the characteristics that, according to various authors, determine that a text is more suitable for a particular translation method, three different text types were analyzed and translated (from English to French) using the three methods listed above in order to establish the correlations between the characteristics of the texts and the method that gave the best results for each text. This data was used to develop a checklist of guidelines and to assign values to their various elements (e.g., the optimal number of words per sentence, the maximum number of sentences in the passive voice). Two pilot tests were then performed to verify the validity of two sets of values for predicting the best method on two new text types. After these texts were translated and the predictions verified, the values were adjusted and a final test was performed. The results of the final test support the conclusion that the approach proposed in this thesis can indeed help text allocators and translators choose the best method to use.