What are the units of text that we want to model? From bytes to multi-word
expressions, text can be analyzed and generated at many granularities. Until
recently, most natural language processing (NLP) models operated over words,
treating those as discrete and atomic tokens, but starting with byte-pair
encoding (BPE), subword-based approaches have become dominant in many areas,
enabling small vocabularies while still allowing for fast inference. Is the end
of the road character-level model or byte-level processing? In this survey, we
connect several lines of work from the pre-neural and neural era, by showing
how hybrid approaches of words and characters as well as subword-based
approaches based on learned segmentation have been proposed and evaluated. We
conclude that there is and likely will never be a silver bullet singular
solution for all applications and that thinking seriously about tokenization
remains important for many applications.

本文通过调研 pre-neural 和 neural era 中的多种技术，探讨自然语言处理中的 “微观结构”（从字节到词组）建模方式是否应采用字符级或字节级处理，或采取分词处理的基于子字的方法，得出结论：没有一种万能的处理方式适用于所有情况，仍需要认真考虑分词对于不同应用场景的重要性。