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High-quality Machine Translation MT evaluation relies heavily on human judgments. Comprehensive error classification methods, such as Multidimensional Quality Metrics MQM , are expensive as they are time-consuming and can only be done by experts, whose availability may be limited especially for low-resource languages. On the other hand, just assigning overall scores, like Direct Assessment DA , is simpler and faster and can be done by translators of any level, but are less reliable.
While automatic evaluation metrics are important and invaluable tools for rapid development of Machine Translation MT systems, human assessment remains the gold standard of translation quality Kocmi et al. The translation quality is conceptually measured through adequacy preservation of the original meaning and fluency grammaticality of the translated text; Koehn and Monz, , and sometimes through comprehension how readable or understandable the translation is; White et al.
Annotators are usually asked to assign a score on a particular quality aspect. Likert and 0— scale are often used for discrete and continuous scales. The most popular scoring method in machine translation field in recent years is Direct Assessment DA; Graham et al. Translation scores indicate the overall quality of a translation, but they can be subjective and do not provide details about the translation errors.
The usual way to overcome this drawback is error classification: asking the evaluators to mark each translation error and assign an error tag from a set of predefined categories, such as terminology or style. While error classification provides interesting insights into the distribution of different types of errors, it requires much more time and effort, both for annotators and task organizers, who need to prepare the error taxonomy, annotation guidelines and training examples.
A simpler alternative in the form of highlighting errors without assigning their error types is a compromise between overall scoring and error classification Kreutzer et al.
