CANINE: Pre-training An Efficient Tokenization-free Encoder For Language Representation

Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. Transactions of the Association for Computational Linguistics 2021 – 41 citations

[Paper]    
Model Architecture Tokenization Ethics and Bias Transformer Reinforcement Learning Pre-Training BERT Training Techniques Evaluation

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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.

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