{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-korean-nlp-tasks-with","title":"Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character Decomposition","arxiv_id":"2311.03928","date":"2023-11-07","proceeding":null,"authors":["Taehee Jeon","BongSeok Yang","ChangHwan Kim","Yoonseob Lim"],"abstract":"We introduce a morpheme-aware subword tokenization method that utilizes sub-character decomposition to address the challenges of applying Byte Pair Encoding (BPE) to Korean, a language characterized by its rich morphology and unique writing system. Our approach balances linguistic accuracy with computational efficiency in Pre-trained Language Models (PLMs). Our evaluations show that this technique achieves good performances overall, notably improving results in the syntactic task of NIKL-CoLA. This suggests that integrating morpheme type information can enhance language models' syntactic and semantic capabilities, indicating that adopting more linguistic insights can further improve performance beyond standard morphological analysis.","url_abs":"https://arxiv.org/abs/2311.03928v1","url_pdf":"https://arxiv.org/pdf/2311.03928v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-korean-nlp-tasks-with","repo_url":"https://github.com/taeheejeon22/morphsubdecomp-korean","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CoLA"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2311.03928","atlas_url":"https://app.syntology.ai/?focus=2311.03928","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}