As NLP tools become ubiquitous in today's technological landscape, they are
increasingly applied to languages with a variety of typological structures.
However, NLP research does not focus primarily on typological differences in
its analysis of state-of-the-art language models. As a result, NLP tools
perform unequally across languages with different syntactic and morphological
structures. Through a detailed discussion of word order typology, morphological
typology, and comparative linguistics, we identify which variables most affect
language modeling efficacy; in addition, we calculate word order and
morphological similarity indices to aid our empirical study. We then use this
background to support our analysis of an experiment we conduct using
multi-class text classification on eight languages and eight models.

研究探讨了 NLP 工具在不同语言的应用情况以及针对语言结构差异进行的分析，指出哪些变量最影响语言建模的效果，通过计算词序和形态相似性指数等方法进行实证研究，最后通过多分类文本分类实验验证了研究结论。