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When translating phrases (words or group of words), human translators,consciously or not, resort to different translation processes apart from theliteral translation, such as Idiom Equivalence, Generalization,Particularization, Semantic Modulation, etc. Translators and linguists (such asVinay and Darbelnet, Newmark, etc.) have proposed several typologies tocharacterize the different translation processes. However, to the best of ourknowledge, there has not been effort to automatically classify thesefine-grained translation processes. Recently, an English-French parallel corpusof TED Talks has been manually annotated with translation process categories,along with established annotation guidelines. Based on these annotatedexamples, we propose an automatic classification of translation processes atsubsentential level. Experimental results show that we can distinguishnon-literal translation from literal translation with an accuracy of 87.09%,and 55.20% for classifying among five non-literal translation processes. Thiswork demonstrates that it is possible to automatically classify translationprocesses. Even with a small amount of annotated examples, our experiments showthe directions that we can follow in future work. One of our long termobjectives is leveraging this automatic classification to better controlparaphrase extraction from bilingual parallel corpora.

Towards Recognizing Phrase Translation Processes: Experiments on English-French