From human to automatic error classification for machine translation output

Maja Popovic; Aljoscha Burchardt

In: 15th International Conference of the European Association for Machine Translation (EAMT 11). Annual Conference of the European Association for Machine Translation (EAMT-11), 15th, May 30-31, Leuven, Belgium, European Association for Machine Translation, 5/2011.


Future improvement of machine translation systems requires reliable automatic evaluation and error classification measures to avoid time and money consuming human classification. In this article, we propose a new method for automatic error classification and systematically compare its results to those obtained by humans. We show that the proposed automatic measures correlate well with human judgments across different error classes as well as across different translation outputs on four out of five commonly used error classes.


Popovic-14-finalVersion.pdf (pdf, 60 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence