Why Self-Supervised Encoders Want to Be Normal

arXiv:2604.27743v1 Announce Type: cross Abstract: We develop a geometric and information-theoretic framework for encoder-decoder learning built on the Information Bottleneck (IB) principle. Recasting IB as

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

arXiv:2604.23948v1 Announce Type: cross
Abstract: The Korean writing system, textitHangeul, has a unique character representation rigidly following the invention principles recorded in textitHunminjeongeum.footnotetextitHunminjeongeum is a book published in 1446 that describes the principles of invention and usage of textitHangeul, devised by King Sejong citeHunminjeongeum_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 textitHangeul 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).

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