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Character-based representations have important advantages over subword-basedones for morphologically rich languages. They come with increased robustness tonoisy input and do not need a separate tokenization step. However, they alsohave a crucial disadvantage: they notably increase the length of textsequences. The GBST method from Charformer groups (aka downsamples) charactersto solve this, but allows information to leak when applied to a Transformerdecoder. We solve this information leak issue, thereby enabling charactergrouping in the decoder. We show that Charformer downsampling has no apparentbenefits in NMT over previous downsampling methods in terms of translationquality, however it can be trained roughly 30% faster. Promising performance onEnglish--Turkish translation indicate the potential of character-level modelsfor morphologically-rich languages.

Patching Leaks in the Charformer for Efficient Character-Level Generation