Simplified Chinese to Traditional Chinese character conversion is a common
preprocessing step in Chinese NLP. Despite this, current approaches have poor
performance because they do not take into account that a simplified Chinese
character can correspond to multiple traditional characters. Here, we propose a
model that can disambiguate between mappings and convert between the two
scripts. The model is based on subword segmentation, two language models, as
well as a method for mapping between subword sequences. We further construct
benchmark datasets for topic classification and script conversion. Our proposed
method outperforms previous Chinese Character conversion approaches by 6 points
in accuracy. These results are further confirmed in a downstream application,
where 2kenize is used to convert pretraining dataset for topic classification.
An error analysis reveals that our method's particular strengths are in dealing
with code-mixing and named entities.

本文提出了一种基于子词分割，两种语言模型以及子词序列映射方法的模型，能够在中文自然语言处理中对简体和繁体中文实现准确的转换，能够有效地解决语种混合和命名实体问题，并在主题分类和脚本转换的基准数据集上取得了 6% 的准确度提升。