  Most current popular subword tokenizers are trained based on word frequency
statistics over a corpus, without considering information about co-occurrence
or context. Nevertheless, the resulting vocabularies are used in language
models' highly contextualized settings. We present SaGe, a tokenizer that
tailors subwords for their downstream use by baking in the contextualized
signal at the vocabulary creation phase. We show that SaGe does a better job
than current widespread tokenizers in keeping token contexts cohesive, while
not incurring a large price in terms of encoding efficiency or domain
robustness. SaGe improves performance on English GLUE classification tasks as
well as on NER, and on Inference and NER in Turkish, demonstrating its
robustness to language properties such as morphological exponence and
agglutination.
