Papers › Cross-lingual Language Model Pretraining

Cross-lingual Language Model Pretraining

22 Jan 2019NeurIPS 2019 12arXiv:1901.07291archive 2025-07-28

Guillaume Lample, Alexis Conneau

Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.

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1-punchMan/CLTS mentioned on GitHubpytorch report
JunnYu/xlm_paddle mentioned on GitHubpaddleApache-2.0 report
Somefive/XNLI mentioned on GitHubpytorch report
Tikquuss/meta_XLM mentioned on GitHubpytorch report
deterministic-algorithms-lab/Large-XLM mentioned on GitHubpytorch report
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facebookresearch/UnsupervisedMT mentioned on GitHubpytorchNOASSERTION report
facebookresearch/XLM mentioned on GitHubpytorch report
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check_different MiuGod0126/ConvS2S_Paddle/align.py community (archive-listed) unverified Apache-2.0 (permissive) · 67fe142c7fcc1801 · report
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Tasks

Language ModelingLanguage ModellingMachine TranslationNatural Language UnderstandingTranslationUnsupervised Machine Translationmodel

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Machine Translation WMT2016 Romanian-English MLM pretraining BLEU score 35.3 #4 of 21 Archive leaderboard report
Natural Language Inference XNLI French XLM (MLM+TLM) Accuracy 80.2 #5 of 6 Archive leaderboard report
Unsupervised Machine Translation WMT2014 English-French MLM pretraining for encoder and decoder BLEU 33.4 #4 of 7 Archive leaderboard report
Unsupervised Machine Translation WMT2014 French-English MLM pretraining for encoder and decoder BLEU 33.3 #4 of 7 Archive leaderboard report
Unsupervised Machine Translation WMT2016 English--Romanian MLM pretraining for encoder and decoder BLEU 33.3 #2 of 2 Archive leaderboard report
Unsupervised Machine Translation WMT2016 English-German MLM pretraining for encoder and decoder BLEU 26.4 #4 of 7 Archive leaderboard report
Unsupervised Machine Translation WMT2016 English-Romanian MLM pretraining for encoder and decoder BLEU 33.3 #2 of 3 Archive leaderboard report
Unsupervised Machine Translation WMT2016 German-English MLM pretraining for encoder and decoder BLEU 34.3 #4 of 7 Archive leaderboard report
Unsupervised Machine Translation WMT2016 Romanian-English MLM pretraining for encoder and decoder BLEU 31.8 #3 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: XLM

AdamAttentionAttention DropoutBPEDense ConnectionsDropoutLayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmaxXLM

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