<scp>Canine</scp>: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent approaches based on data-derived subword lexicons are less brittle than manually engineered tokenizers, these techniques not equally suited to languages, and the use of any fixed vocabulary may limit a model's ability adapt. In this paper, we present CANINE, encoder that operates directly character sequences, without or vocabulary, pre-training strategy either characters optionally uses subwords as soft inductive bias. To its finer-grained input effectively efficiently, CANINE combines downsampling, which reduces sequence length, with deep transformer stack, encodes context. outperforms comparable mBERT model 2.8 F1 TyDi QA, challenging multilingual benchmark, despite having 28% fewer parameters.