Source language markers in EUROPARL translations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Evaluation of several phonetic similarity algorithms on the task of cognate identification
LD '06 Proceedings of the Workshop on Linguistic Distances
Identification of translationese: a machine learning approach
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Adapting translation models to translationese improves SMT
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Discourse structure and language technology
Natural Language Engineering
Language models for machine translation: Original vs. translated texts
Computational Linguistics
Improving statistical machine translation by adapting translation models to translationese
Computational Linguistics
Improving statistical machine translation by adapting translation models to translationese
Computational Linguistics
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While it is has often been observed that the product of translation is somehow different than non-translated text, scholars have emphasized two distinct bases for such differences. Some have noted interference from the source language spilling over into translation in a source-language-specific way, while others have noted general effects of the process of translation that are independent of source language. Using a series of text categorization experiments, we show that both these effects exist and that, moreover, there is a continuum between them. There are many effects of translation that are consistent among texts translated from a given source language, some of which are consistent even among texts translated from families of source languages. Significantly, we find that even for widely unrelated source languages and multiple genres, differences between translated texts and non-translated texts are sufficient for a learned classifier to accurately determine if a given text is translated or original.