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        <title>Are All Languages Equally Hard to Language-Model?</title>
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        <namePart type="given">Ryan</namePart>
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        <namePart type="given">Sabrina</namePart>
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
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            <namePart type="given">Marilyn</namePart>
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            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
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    <abstract>For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.</abstract>
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    <identifier type="doi">10.18653/v1/N18-2085</identifier>
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%0 Conference Proceedings
%T Are All Languages Equally Hard to Language-Model?
%A Cotterell, Ryan
%A Mielke, Sabrina J.
%A Eisner, Jason
%A Roark, Brian
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F cotterell-etal-2018-languages
%X For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.
%R 10.18653/v1/N18-2085
%U https://aclanthology.org/N18-2085
%U https://doi.org/10.18653/v1/N18-2085
%P 536-541

Markdown (Informal)

[Are All Languages Equally Hard to Language-Model?](https://aclanthology.org/N18-2085) (Cotterell et al., NAACL 2018)

• Are All Languages Equally Hard to Language-Model? (Cotterell et al., NAACL 2018)

ACL

• Ryan Cotterell, Sabrina J. Mielke, Jason Eisner, and Brian Roark. 2018. Are All Languages Equally Hard to Language-Model?. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 536–541, New Orleans, Louisiana. Association for Computational Linguistics.