  Pipelined NLP systems have largely been superseded by end-to-end neural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