Cross-lingual Language Model Pretraining

NeurIPS 2019  ·  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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Unsupervised Machine Translation WMT2014 English-French MLM pretraining for encoder and decoder BLEU 33.4 # 4
Unsupervised Machine Translation WMT2014 French-English MLM pretraining for encoder and decoder BLEU 33.3 # 4
Unsupervised Machine Translation WMT2016 English-German MLM pretraining for encoder and decoder BLEU 26.4 # 4
Unsupervised Machine Translation WMT2016 English-Romanian MLM pretraining for encoder and decoder BLEU 33.3 # 2
Unsupervised Machine Translation WMT2016 English--Romanian MLM pretraining for encoder and decoder BLEU 33.3 # 2
Unsupervised Machine Translation WMT2016 German-English MLM pretraining for encoder and decoder BLEU 34.3 # 4
Unsupervised Machine Translation WMT2016 Romanian-English MLM pretraining for encoder and decoder BLEU 31.8 # 3
Machine Translation WMT2016 Romanian-English MLM pretraining BLEU score 35.3 # 3
Natural Language Inference XNLI French XLM (MLM+TLM) Accuracy 80.2 # 4

Methods