@inproceedings{cihlar-etal-2026-investigating,
title = "Investigating the Automatic Translation of {K}orean Honorifics",
author = "Cihlar, Luis and
Bui, Minh Duc and
Park, Kyung eun and
Mager, Manuel and
Bisang, Walter and
von der Wense, Katharina",
editor = "Pranav, A and
Basile, Valerio and
Falk, Neele and
Jurgens, David and
Lapesa, Gabriella and
Lauscher, Anne and
Lo, Soda Marem",
booktitle = "Proceedings of the Second Workshop of Identity Aware {AI}",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2026.iaai-1.3/",
doi = "10.63317/5g3ca2zi2jf6",
pages = "20--31",
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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<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.</abstract>
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%0 Conference Proceedings
%T Investigating the Automatic Translation of Korean Honorifics
%A Cihlar, Luis
%A Bui, Minh Duc
%A Park, Kyung eun
%A Mager, Manuel
%A Bisang, Walter
%A von der Wense, Katharina
%Y Pranav, A.
%Y Basile, Valerio
%Y Falk, Neele
%Y Jurgens, David
%Y Lapesa, Gabriella
%Y Lauscher, Anne
%Y Lo, Soda Marem
%S Proceedings of the Second Workshop of Identity Aware AI
%D 2026
%8 May
%I European Language Resources Association
%C Palma de Mallorca, Spain
%F cihlar-etal-2026-investigating
%X 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.
%R 10.63317/5g3ca2zi2jf6
%U https://aclanthology.org/2026.iaai-1.3/
%U https://doi.org/10.63317/5g3ca2zi2jf6
%P 20-31
Markdown (Informal)
[Investigating the Automatic Translation of Korean Honorifics](https://aclanthology.org/2026.iaai-1.3/) (Cihlar et al., iaai 2026)
ACL
- Luis Cihlar, Minh Duc Bui, Kyung eun Park, Manuel Mager, Walter Bisang, and Katharina von der Wense. 2026. Investigating the Automatic Translation of Korean Honorifics. In Proceedings of the Second Workshop of Identity Aware AI, pages 20–31, Palma de Mallorca, Spain. European Language Resources Association.