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.
@article{arxiv.2311.03928,
title = {Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character Decomposition},
author = {Taehee Jeon and Bongseok Yang and Changhwan Kim and Yoonseob Lim},
journal= {arXiv preprint arXiv:2311.03928},
year = {2023}
}