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KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters

About

The Korean writing system, \textit{Hangeul}, has a unique character representation rigidly following the invention principles recorded in \textit{Hunminjeongeum}.\footnote{\textit{Hunminjeongeum} is a book published in 1446 that describes the principles of invention and usage of \textit{Hangeul}, devised by King Sejong \cite{Hunminjeongeum_Guide}.} However, existing pre-trained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called KOMBO, which firstly brings the invention principles of \textit{Hangeul} to represent character. Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state-of-the-art Korean PLM by an average of 2.11\% in five Korean natural language understanding tasks. Furthermore, extensive experiments demonstrate that our proposed method is suitable for comprehending the linguistic features of the Korean language. Consequently, we shed light on the superiority of using subcharacters over the typical subword-based approach for Korean PLMs. Our code is available at: [https://github.com/SungHo3268/KOMBO](https://github.com/SungHo3268/KOMBO).

SungHo Kim, Juhyeong Park, Yeachan Kim, SangKeun Lee• 2026

Related benchmarks

TaskDatasetResultRank
Paraphrase IdentificationPAWS-X (test)
Accuracy70.88
22
Machine Reading ComprehensionKorQuAD 1.0 (dev)
Exact Match (EM)77.47
13
Natural Language InferenceKorNLI (dev)
Accuracy74.22
13
Natural Language InferenceKorNLI (test)
Accuracy73.77
13
Semantic Textual SimilarityKorSTS (dev)
Spearman Correlation (100x)84.47
13
Semantic Textual SimilarityKorSTS (test)
Spearman Correlation77.43
13
Sentiment AnalysisNSMC (dev)
Accuracy88.71
13
Sentiment AnalysisNSMC (test)
Accuracy0.887
13
Natural Language InferenceKorNLI
Clean Accuracy73.77
5
Offensive Language DetectionBEEP
Precision90.66
5
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