Prior studies in multilingual language modeling (e.g., Cotterell et al.,
2018; Mielke et al., 2019) disagree on whether or not inflectional morphology
makes languages harder to model. We attempt to resolve the disagreement and
extend those studies. We compile a larger corpus of 145 Bible translations in
92 languages and a larger number of typological features. We fill in missing
typological data for several languages and consider corpus-based measures of
morphological complexity in addition to expert-produced typological features.
We find that several morphological measures are significantly associated with
higher surprisal when LSTM models are trained with BPE-segmented data. We also
investigate linguistically-motivated subword segmentation strategies like
Morfessor and Finite-State Transducers (FSTs) and find that these segmentation
strategies yield better performance and reduce the impact of a language's
morphology on language modeling.

本文通过对 145 个语言的 92 个圣经翻译及其更多的分类特征的分析，探讨了语言的屈折变化是否使得多语言语言建模更加困难，发现基于神经网络的语言建模方法可以通过采用 FST 等分词策略来进一步降低语言构型的影响。