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ACL Findings2024

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

SungHo Kim, Juhyeong Park, Yeachan Kim, SangKeun Lee

Abstract

The Korean writing system, Hangeul, has a unique character representation rigidly following the invention principles recorded in Hunminjeongeum. 1 However, existing pretrained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called , which firstly brings the invention principles of Hangeul to represent character. Our proposed method, , exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state-ofthe-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.

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Cited by top-tier papers2

  1. Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in KoreanSungHo Kim, Nayeon Kim, Taehee Jeon, SangKeun LeeACL 2025
  2. HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-TrainingSeungho Choi, Sihyun Park, Minsang Kim, Chansol Park et al.AAAI 2026

Builds on5

  1. Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language ProcessingZihang Dai, Guokun Lai, Yiming Yang, Quoc LeNeurIPS 2020 · 273 citations
  2. KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language UnderstandingJiyeon Ham, Yo Joong Choe, Kyubyong Park, Ilji Choi et al.EMNLP Findings 2020 · 62 citations
  3. KOLD: Korean Offensive Language DatasetYounghoon Jeong, Juhyun Oh, Jongwon Lee, Jaimeen Ahn et al.EMNLP 2022 · 41 citations
  4. What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained TransformersBoseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee et al.EMNLP 2021 · 11 citations
  5. Break it Down into BTS: Basic, Tiniest Subword Units for KoreanNayeon Kim, Jun-Hyung Park, Joon-Young Choi, Eojin Jeon et al.EMNLP 2022 · 3 citations

Related papers

  1. SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language ModelsSungHo Kim, Juhyeong Park, Eda Atalay, SangKeun LeeACL Findings 2026
  2. EXECUTE: A Multilingual Benchmark for LLM Token UnderstandingLukas Edman, Helmut Schmid, Alexander FraserACL Findings 2025 · 1 citation
  3. ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationZijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng et al.ACL 2021
  4. Knowledge Prompting in Pre-trained Language Model for Natural Language UnderstandingJianing Wang, Wenkang Huang, Minghui Qiu, Qiuhui Shi et al.EMNLP 2022 · 26 citations
  5. Beyond Distribution: Investigating Language Models' Understanding of Sino-Korean MorphemesTaehee JeonEMNLP Findings 2025
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