KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters
- URL: http://arxiv.org/abs/2604.23948v1
- Date: Mon, 27 Apr 2026 01:53:52 GMT
- Title: KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters
- Abstract summary: The Korean writing system, textitHangeul, has a unique character representation rigidly following the invention principles recorded in textitHunminjeongeum.footnotetextitHunminjeongeum.<n>We introduce a novel framework for Korean pre-trained language models (PLMs) called KOMBO.<n>Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks.
- Score: 20.335880983366508
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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).
Related papers
- SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models [11.111389249714533]
SCRIPT is a model-agnostic module that injects subcharacter compositional knowledge into Korean PLMs.<n>SCRIPT enhances all baselines across various Korean natural language understanding (NLU) and generation (NLG) tasks.
arXiv Detail & Related papers (2026-04-14T07:09:44Z) - A Curious Class of Adpositional Multiword Expressions in Korean [10.449742937121014]
Multiword expressions (MWEs) have been widely studied in cross-lingual annotation frameworks such as PARSEME.<n>In this paper, we study a class of Korean functional multiword expressions: postpositional verb-based constructions (PVCs)
arXiv Detail & Related papers (2026-02-17T21:23:16Z) - Open Korean Historical Corpus: A Millennia-Scale Diachronic Collection of Public Domain Texts [52.754009498236684]
We introduce the Open Korean Historical Corpus, a dataset spanning 1,300 years and 6 languages.<n>This corpus contains 18 million documents and 5 billion tokens from 19 sources, ranging from the 7th century to 2025.<n>This work provides a foundational resource for quantitative diachronic analysis by capturing the history of the Korean language.
arXiv Detail & Related papers (2025-10-28T15:43:26Z) - Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization [48.05034213634496]
We propose a generative framework that explicitly models the mnemonic construction process as driven by a set of common rules.<n>Our method learns latent structures and compositional rules, enabling interpretable and systematic mnemonics generation.
arXiv Detail & Related papers (2025-07-07T15:49:23Z) - Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning [11.555822066922504]
We introduce KOPL, a novel framework for handling Korean OOV words with Phoneme representation Learning.<n>We empirically demonstrate that KOPL significantly improves the performance on Korean Natural Language Processing (NLP) tasks.
arXiv Detail & Related papers (2025-07-05T12:16:55Z) - Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers [7.275938266030414]
Syntactic elements, such as word order and case markers, are fundamental in natural language processing.
This study explores whether Korean language models can accurately capture this flexibility.
arXiv Detail & Related papers (2024-07-12T11:33:41Z) - A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
Natural language inference is a proxy for natural language understanding.
There is no publicly available NLI corpus for the Romanian language.
We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
arXiv Detail & Related papers (2024-05-20T08:41:15Z) - KoCoNovel: Annotated Dataset of Character Coreference in Korean Novels [0.0]
KoCoNovel is a novel character coreference dataset derived from Korean literary texts.
One of KoCoNovel's distinctive features is that 24% of all character mentions are single common nouns.
arXiv Detail & Related papers (2024-04-01T14:36:35Z) - Efficient and Effective Vocabulary Expansion Towards Multilingual Large
Language Models [9.359647125218359]
This report introduces textttEEVE-Korean-v1.0, a Korean adaptation of large language models.
Our method can significantly boost non-English proficiency within just 2 billion tokens.
arXiv Detail & Related papers (2024-02-22T17:12:39Z) - Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language
Pre-training [50.100992353488174]
We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters.
We name the two core modules of CDBERT as Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most appropriate meaning from Chinese dictionaries.
Our paradigm demonstrates consistent improvements on previous Chinese PLMs across all tasks.
arXiv Detail & Related papers (2023-05-30T05:48:36Z) - SHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language
Model Pretraining [48.880840711568425]
We study the influences of three main factors on the Chinese tokenization for pretrained language models.
We propose three kinds of tokenizers: SHUOWEN (meaning Talk Word), the pronunciation-based tokenizers; 2) JIEZI (meaning Solve Character), the glyph-based tokenizers.
We find that SHUOWEN and JIEZI tokenizers can generally outperform conventional single-character tokenizers.
arXiv Detail & Related papers (2021-06-01T11:20:02Z) - KLUE: Korean Language Understanding Evaluation [43.94952771238633]
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
KLUE is a collection of 8 Korean natural language understanding (NLU) tasks.
We build all of the tasks from scratch from diverse source corpora while respecting copyrights.
arXiv Detail & Related papers (2021-05-20T11:40:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.