Towards Recognizing Phrase Translation Processes: Experiments on English-French

TL;DR

This paper presents an automatic method to classify translation processes in English-French phrase translation, distinguishing literal from non-literal translations with high accuracy, based on annotated TED Talk data.

Contribution

It introduces the first automatic classification approach for fine-grained translation processes at the subsentential level using a small annotated corpus.

Findings

87.09% accuracy in distinguishing non-literal from literal translation

55.20% accuracy in classifying among five non-literal translation processes

Demonstrates feasibility of automatic translation process classification

Abstract

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