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arXiv · 1806.03743

Are All Languages Equally Hard to Language-Model?

Ryan Cotterell·Sabrina J. Mielke·Jason Eisner·Brian Roark
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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.

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Ryan Cotterell, Sabrina J. Mielke, Jason Eisner, Brian Roark. 2018-06-10. Are All Languages Equally Hard to Language-Model?. https://arxiv.org/abs/1806.03743

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