Investigating the Automatic Translation of Korean Honorifics

Abstract

Honorifics encode social hierarchies and relational nuances, making their correct use a culturally sensitive yet challenging aspect of translation. In doing so, they reflect and shape how individuals position themselves and others within a social world. In this work, we investigate how different translation models handle Korean honorifics, both in implicit scenarios, where only the sentence is given, and explicit scenarios. Our findings are as follows: (i) large language models (LLMs) fine-tuned for translation (MTLMs) consistently prefer polite forms more than their instruction-tuned counterparts in both scenarios; (ii) sequence-to-sequence models produce less polite outputs in implicit contexts but shift toward more polite forms when the addressee is explicitly provided; and (iii) both types of LM-based models tend to become more casual when the addressee is known. When compared with human preferences, MTLMs diverge more strongly, exhibiting a systematic overuse of polite forms relative to human judgments.

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