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Computer Science > Computation and Language

arXiv:2012.06262 (cs)
[Submitted on 11 Dec 2020]

Title:Morphology Matters: A Multilingual Language Modeling Analysis

Authors:Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, Lane Schwartz
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Abstract: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.
Comments: To appear in TACL, a pre-MIT Press publication version; 15 pages, 3 figures; for the datasets, see this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2012.06262 [cs.CL]
  (or arXiv:2012.06262v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2012.06262
Journal reference: Transactions of the Association for Computational Linguistics 9 (2021) 261-276
Related DOI: https://doi.org/10.1162/tacl_a_00365

Submission history

From: Hyunji Park [view email]
[v1] Fri, 11 Dec 2020 11:55:55 UTC (541 KB)
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