@inproceedings{hwang-etal-2024-kosmic-korean,
title = "Kosmic: {K}orean Text Similarity Metric Reflecting Honorific Distinctions",
author = "Hwang, Yerin and
Kim, Yongil and
Bae, Hyunkyung and
Bang, Jeesoo and
Lee, Hwanhee and
Jung, Kyomin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.870",
pages = "9954--9960",
abstract = "Existing English-based text similarity measurements primarily focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean, where honorific expressions are explicitly integrated. To address this limitation, this study proposes Kosmic, a novel Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. For the evaluation, we introduce a novel benchmark annotated by human experts, empirically showing that Kosmic outperforms the existing method. Moreover, by leveraging Kosmic, we assess various Korean paraphrasing methods to determine which techniques are most effective in preserving semantics and tone.",
}
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%0 Conference Proceedings
%T Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions
%A Hwang, Yerin
%A Kim, Yongil
%A Bae, Hyunkyung
%A Bang, Jeesoo
%A Lee, Hwanhee
%A Jung, Kyomin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F hwang-etal-2024-kosmic-korean
%X Existing English-based text similarity measurements primarily focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean, where honorific expressions are explicitly integrated. To address this limitation, this study proposes Kosmic, a novel Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. For the evaluation, we introduce a novel benchmark annotated by human experts, empirically showing that Kosmic outperforms the existing method. Moreover, by leveraging Kosmic, we assess various Korean paraphrasing methods to determine which techniques are most effective in preserving semantics and tone.
%U https://aclanthology.org/2024.lrec-main.870
%P 9954-9960
Markdown (Informal)
[Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions](https://aclanthology.org/2024.lrec-main.870) (Hwang et al., LREC-COLING 2024)
ACL