Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have insufficient 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.

Ranking functions in information retrieval are often used in search engines to extract the relevant answers to the query. This paper makes use of this notion of information retrieval and applies onto the problem domain of cognate detection. The main contributions of this paper are: (1) positional tokenization, which incorporates the sequential notion; (2) graphical error modelling, which calculates the morphological shifts. The current research work only distinguishes whether a pair of words are cognates or not. However, we also study if we could predict a possible cognate from the given input. Our study shows that language modelling based retrieval functions with positional tokenization and error modelling tend to give better results than competing baselines.