From Human to Automatic Error Classification for Machine Translation Output
Maja Popoviand, Aljoscha Burchardt · 2011
Future improvement of machine transla-tion systems requires reliable automatic evaluation and error classification mea-sures to avoid time and money consuming human classification. In this article, we propose a new method for automatic er-ror classification and systematically com-pare its results to those obtained by hu-mans. We show that the proposed auto-matic 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.