Due to the widespread use of data-driven neural machine translation, both by professional translators and layperson users, an adequate machine translation literacy on the part of the users of this technology is becoming more and more important. At the same time, the increasing datafication of both the private and the business sphere requires an adequate data literacy in modern society. The present article takes a closer look at machine translation literacy and data literacy and investigates the interface between the two concepts. This is done to lay the preliminary theoretical foundations for a didactic project aiming to develop didactic resources for teaching data literacy in its machine translation-specific form to students of BA programmes in translation/specialised communication.
We present various empirical methods and tools that can objectify and optimize the evaluation of translated texts as parallel translation corpora produced by professional translators or by a machine translation service. The proposed methods and tools are based on an empirical analysis of information processing in translated texts and on the utilitarian role of machine translation and its methods, and they can be implemented in a tool-based translation evaluation apparatus in a professional context. The salient part of these methods (which can be deployed automatically or manually) relies on the comparison of two parameters that are measurable in most natural languages, namely the length of the segments in characters and the number of lexical words they contain. Our recent work (Poirier, 2017 and Poirier, 2021) has shown that these parameters have a strong positive correlation in translation (above 0.9 as a rule, and most often exceeding 0.95): the more characters or lexical words the source segment contains, the more characters or lexical words the translation contains. The measurement of the lexical words and the information volume of the translations allows distinguishing heteromorphic translations (more or less information) from isomorphic segments (same information content). The manual and partially automatic analysis of heteromorphic segments opens up new empirical horizons in the professional evaluation of translations as well as in the contrastive study of discourse […]
