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        <title>Morphology Matters: A Multilingual Language Modeling Analysis</title>
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    <name type="personal">
        <namePart type="given">Hyunji</namePart>
        <namePart type="given">Hayley</namePart>
        <namePart type="family">Park</namePart>
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        <namePart type="given">Katherine</namePart>
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        <namePart type="family">Zhang</namePart>
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    <name type="personal">
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    <abstract>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.1 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.</abstract>
    <identifier type="citekey">park-etal-2021-morphology</identifier>
    <identifier type="doi">10.1162/tacl_a_00365</identifier>
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        <date>2021</date>
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%0 Journal Article
%T Morphology Matters: A Multilingual Language Modeling Analysis
%A Park, Hyunji Hayley
%A Zhang, Katherine J.
%A Haley, Coleman
%A Steimel, Kenneth
%A Liu, Han
%A Schwartz, Lane
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F park-etal-2021-morphology
%X 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.1 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.
%R 10.1162/tacl_a_00365
%U https://aclanthology.org/2021.tacl-1.16
%U https://doi.org/10.1162/tacl_a_00365
%P 261-276

Markdown (Informal)

[Morphology Matters: A Multilingual Language Modeling Analysis](https://aclanthology.org/2021.tacl-1.16) (Park et al., TACL 2021)

• Morphology Matters: A Multilingual Language Modeling Analysis (Park et al., TACL 2021)

ACL

• Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, and Lane Schwartz. 2021. Morphology Matters: A Multilingual Language Modeling Analysis. Transactions of the Association for Computational Linguistics, 9:261–276.