engleski

Clash between Segment-level MT Error Analysis and Selected Lexical Similarity Metrics

The aim of this paper is to evaluate the quality of popular machine translation engines on three texts of different genre in a scenario in which both source and target languages are morphologically rich. Translations are obtained from Google Translate and Microsoft Bing engines and German-Croatian is selected as the language pair. The analysis entails both human and automatic evaluation. The process of error analysis, which is time-consuming and often tiresome, is conducted in the userfriendly Windows 10 application TREAT. Prior to annotation, training is conducted in order to familiarize the annotator with MQM, which is used in the annotation task, and the interface of TREAT. The annotation guidelines elaborated with examples are provided. The evaluation is also conducted with automatic metrics BLEU and CHRF++ in order to assess their segmentlevel correlation with human annotations on three different levels–accuracy, mistranslation, and the total number of errors. Our findings indicate that neither the total number of errors, nor the most prominent error category and subcategory, show consistent and statistically significant segment-level correlation with the selected automatic metrics.

Machine translation ; evaluation ; error analysis ; BLEU ; CHRF++ ; MQM

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