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Status: Unchecked ×Keyword: cross-lingual representation learning ×Clear all

3 claims from 1 paper

1. Computer Science › Topic Modeling

   Unsupervised Cross-lingual Representation Learning at Scale

   Conneau, Khandelwal, Goyal et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 2020

   The authors trained a masked language model, XLM-R, on one hundred languages using over two terabytes of CommonCrawl data, and report gains on cross-lingual tasks, including for low-resource languages.

   Unchecked3 claims
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   1. UncheckedXLM-R, a multilingual language model, scores higher than multilingual BERT on several cross-lingual tests: +14.6% on XNLI, +13% on MLQA, +2.4% on NER.“Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER.”
   2. UncheckedThe paper says one multilingual model, XLM-R, can match strong single-language models on the GLUE and XNLI benchmarks without losing per-language performance.“Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks.”
   3. Unchecked“XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models.”