Incorporating Context into Subword Vocabularies

TL;DR

SaGe is a novel tokenizer that incorporates contextual information during vocabulary creation, leading to improved language model performance across multiple tasks and languages without significant efficiency loss.

Contribution

This paper introduces SaGe, a context-aware subword tokenizer that enhances token cohesion and model performance in diverse linguistic settings.

Findings

SaGe outperforms traditional tokenizers on English GLUE tasks.

SaGe improves NER and inference in Turkish.

SaGe maintains efficiency and domain robustness.

Abstract

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.