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

• Published in: Transactions of the Association for Computational Linguistics Vol. 10; pp. 73 - 91
• Main Authors: Clark, Jonathan H., Garrette, Dan, Turc, Iulia, Wieting, John
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 31.01.2022; MIT Press Journals, The; The MIT Press
• Subjects: Algorithms; Coders; Language; Linguistics; Morphology; Training; Vocabulary
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00448

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly used models still require an explicit tokenization step.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 , 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, combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes context. outperforms a comparable m model by 5.7 F1 on , a challenging multilingual benchmark, despite having fewer model parameters.