Evolving Fuzzy Models for Automated Translation
Acta Polytechnica Hungarica · 2017
This paper targets two goals.First, it analyzes the most common errors in automated translation from French to English and from English to French performed by a statistical and a hybrid translation engine with the help of the evaluation metric SAE J2450.The test of concordance is applied in order to study the agreement between the original text and its retroversion within the same translation memory software.Seven categories of primary errors are considered, which cover the fields of terminology, semantics, structure, orthography, punctuation and completeness.Second, evolving fuzzy models are developed, which give the overall paragraph score using the seven categories of primary errors as inputs.The fuzzy models permit the users to establish the accuracy of translation and to grade the quality of translations resulted from the reintroduction of the result of translation in the same software application.They also allow the comparison of two popular translation memory programs, namely Google Translate (GT) and Systran, in the framework of the issues of concordance in translation and artificial learning.