A Pilot Study of Multi-Method Evaluation of Machine Translation in Macedonian.

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Bibliographic Details
Title:	A Pilot Study of Multi-Method Evaluation of Machine Translation in Macedonian.
Authors:	Jana, Kuzmanova¹ jana.kuzmanova@finki.ukim.mk, Katerina, Zdravkova¹ katerina.zdravkova@finki.ukim.mk
Source:	Computer Science & Information Systems. Apr2026, Vol. 23 Issue 2, p827-859. 33p.
Subjects:	Evaluation methodology, Machine translating, Low-resource languages, Translating & interpreting, Slavic languages, Generative pre-trained transformers, Linguistic analysis
Abstract:	This pilot study offers a linguistic evaluation of six machine translation systems: GPT-4o, GPT-5, Gemini 2.5 Flash, Google Translate, Microsoft Translator, and NLLB-600M applied to the translation of a short excerpt of Orwell’s "1984" into Macedonian. The analysis consisted of three interconnected experiments: manual annotation of translation errors and comparison with human output, evaluation using eight popular MT metrics, and sentence-level similarity analysis via cosine similarity, Jaccard similarity, and Levenshtein distance. Manual annotation revealed that stylistic errors (48.47%) and linguistic errors (34.54%) were the most common. The LLMs outperformed other systems, particularly GPT-5, while NLLB-600M performed poorly, often introducing incomprehensible sentences or non-existent words. Metrics-based evaluation showed that lexical metrics sometimes penalized fluent and accurate translations that deviated from the reference. Sentence similarity analysis confirmed that accurate translations were more consistent, while wrong–wrong sentence pairs were more divergent, especially in Levenshtein scores. The findings underscore the importance of combining manual and metric-based evaluation to fully understand MT quality, particularly in low-resource settings. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This pilot study offers a linguistic evaluation of six machine translation systems: GPT-4o, GPT-5, Gemini 2.5 Flash, Google Translate, Microsoft Translator, and NLLB-600M applied to the translation of a short excerpt of Orwell’s "1984" into Macedonian. The analysis consisted of three interconnected experiments: manual annotation of translation errors and comparison with human output, evaluation using eight popular MT metrics, and sentence-level similarity analysis via cosine similarity, Jaccard similarity, and Levenshtein distance. Manual annotation revealed that stylistic errors (48.47%) and linguistic errors (34.54%) were the most common. The LLMs outperformed other systems, particularly GPT-5, while NLLB-600M performed poorly, often introducing incomprehensible sentences or non-existent words. Metrics-based evaluation showed that lexical metrics sometimes penalized fluent and accurate translations that deviated from the reference. Sentence similarity analysis confirmed that accurate translations were more consistent, while wrong–wrong sentence pairs were more divergent, especially in Levenshtein scores. The findings underscore the importance of combining manual and metric-based evaluation to fully understand MT quality, particularly in low-resource settings. [ABSTRACT FROM AUTHOR]
ISSN:	18200214
DOI:	10.2298/CSIS251020021K