Tokenization with Factorized Subword Encoding
David Samuel Lilja Øvrelid
In recent years, language models have become increasingly larger and more
complex. However, the input representations for these models continue to rely
on simple and greedy subword tokenization methods. In this paper, we propose a
novel tokenization method that factorizes subwords onto discrete triplets using
a VQ-VAE model. The effectiveness of the proposed tokenization method, referred
to as the Factorizer, is evaluated on language modeling and morpho-syntactic
tasks for 7 diverse languages. Results indicate that this method is more
appropriate and robust for morphological tasks than the commonly used byte-pair
encoding (BPE) tokenization algorithm.