Eva Schlinger


How Multilingual is Multilingual BERT?
Telmo Pires | Eva Schlinger | Dan Garrette
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.


Synthesizing Compound Words for Machine Translation
Austin Matthews | Eva Schlinger | Alon Lavie | Chris Dyer
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


The CMU Machine Translation Systems at WMT 2014
Austin Matthews | Waleed Ammar | Archna Bhatia | Weston Feely | Greg Hanneman | Eva Schlinger | Swabha Swayamdipta | Yulia Tsvetkov | Alon Lavie | Chris Dyer
Proceedings of the Ninth Workshop on Statistical Machine Translation


Translating into Morphologically Rich Languages with Synthetic Phrases
Victor Chahuneau | Eva Schlinger | Noah A. Smith | Chris Dyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing