  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.
