“CANINE: Pre-Training an Efficient Tokenization-Free Encoder for Language Representation”, 2021-03-11 (; similar):
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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 TyDiQA, a challenging multilingual benchmark, despite having 28% fewer model parameters.