CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
This addresses the limitation of fixed vocabularies in multilingual NLP, offering a more adaptable and efficient approach for diverse languages.
The paper tackles the problem of explicit tokenization in NLP models by introducing CANINE, a tokenization-free encoder that operates directly on character sequences, achieving a 2.8 F1 improvement over mBERT on the TyDi QA benchmark with 28% fewer parameters.
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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.