Are All Languages Equally Hard to Language-Model?

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


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.
Anthology ID:
N18-2085
Original:
N18-2085v1
Version 2:
N18-2085v2
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
536–541
Language:
URL:
https://aclanthology.org/N18-2085
DOI:
10.18653/v1/N18-2085
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N18-2085.pdf
Poster:
 N18-2085.Poster.pdf