CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Clark, J. H., Garrette, D., Turc, I., & Wieting, J. arXiv:2103.06874 [cs], March, 2021. arXiv: 2103.06874

Paper abstract bibtex

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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

@article{clark_canine_2021,
	title = {{CANINE}: {Pre}-training an {Efficient} {Tokenization}-{Free} {Encoder} for {Language} {Representation}},
	shorttitle = {{CANINE}},
	url = {http://arxiv.org/abs/2103.06874},
	abstract = {Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.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 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.},
	urldate = {2022-02-15},
	journal = {arXiv:2103.06874 [cs]},
	author = {Clark, Jonathan H. and Garrette, Dan and Turc, Iulia and Wieting, John},
	month = mar,
	year = {2021},
	note = {arXiv: 2103.06874},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

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