Abstract
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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 with soft inductive biases in place of hard token this http URL use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes con-text. CANINE outperforms a comparable mBERT model by >=1 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.
Abstract (translated)
URL
https://arxiv.org/abs/2103.06874
