Why don't people use character-level machine translation?
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We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT). Despite evidence in the literature that character-level systems are comparable with subword systems, they are virtually never used in competitive setups in WMT competitions. We empirically show that even with recent modeling innovations in character-level natural language processing, character-level MT systems still struggle to match their subword-based counterparts both in terms of translation quality and training and inference speed. Character-level MT systems show neither better domain robustness, nor better morphological generalization, despite being often so motivated. On the other hand, they tend to be more robust towards source side noise and the translation quality does not degrade with increasing beam size at decoding time.
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