Automatic Detection of Translated Text and its Impact on Machine Translation
David Kurokawa, Cyril Goutte, Pierre Isabelle · NPARC · 2009
We investigate the possibility of automatically detecting whether a piece of text is an original or a translation. On a large parallel English-French corpus where reference information is available, we find that this is possible with around 90% accuracy. We further study the implication this has on Machine Translation performance. After separating our corpus according to translation direction, we train direction-specific phrase-based MT systems and show that they yield improved translation performance. This suggests that taking directionality into account when training SMT systems may have a significant effect on output quality.