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This article offers an empirical study on the different ways of encodingChinese, Japanese, Korean (CJK) and English languages for text classification.Different encoding levels are studied, including UTF-8 bytes, characters,words, romanized characters and romanized words. For all encoding levels,whenever applicable, we provide comparisons with linear models, fastText andconvolutional networks. For convolutional networks, we compare between encodingmechanisms using character glyph images, one-hot (or one-of-n) encoding, andembedding. In total there are 473 models, using 14 large-scale textclassification datasets in 4 languages including Chinese, English, Japanese andKorean. Some conclusions from these results include that byte-level one-hotencoding based on UTF-8 consistently produces competitive results forconvolutional networks, that word-level n-grams linear models are competitiveeven without perfect word segmentation, and that fastText provides the bestresult using character-level n-gram encoding but can overfit when the featuresare overly rich.

Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?