Machine translation for named entities: error analysis based on its mechanism.

This study examines the characteristics and quality of machine translation (MT) for named entities (NEs). We first provided an overview of the underlying mechanisms and technical obstacles involved in NE translation, based on previous studies in this field to understand the challenges that MTs may encounter when translating NEs. Based on these theoretical considerations, we proposed three hypotheses regarding potential NE-related translation problems and error types: (1) MTs may struggle to translate NEs consistently; (2) MTs often fail to differentiate proper nouns from common nouns; and (3) without sufficient contextual support, low-frequency NEs may be translated incorrectly, resulting in non-words or semantically unrelated terms. We performed a statistical analysis of 120 translations produced by two standard MT systems (Google Translate and Papago) and two generative artificial intelligence models (ChatGPT 3.5 and ClovaX) to test these hypotheses. The results revealed that (1) consistency errors occurred in 4.21%–26%of all NE translations, (2) 19.55%–50.34% of proper nouns that could be confused with common nouns were incorrectly translated as such, and (3) 31.31%–35.72%of low-frequency NEs lacking sufficient context were mistranslated.