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.

我们介绍了一种考虑词素的子词切分方法，利用字符的分解方法来解决字节对编码（BPE）在韩语中应用所面临的挑战，韩语的特点是丰富的语态和独特的书写系统。我们的方法在预训练语言模型（PLMs）中平衡了语言准确性和计算效率。我们的评估结果显示，该技术在整体上表现良好，显著提高了 NIKL-CoLA 句法任务的结果。这表明融合词素类型信息可以增强语言模型的句法和语义能力，表示采用更多的语言洞察力可以进一步提高性能，超越标准的形态分析。