XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora. Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Zero-Shot Cross-Lingual Transfer XTREME Turing ULR v6 Sentence-pair Classification 91.0 # 1
Structured Prediction 83.8 # 2
Question Answering 77.1 # 1
Sentence Retrieval 94.4 # 1
Avg 85.5 # 1
Zero-Shot Cross-Lingual Transfer XTREME Turing ULR v5 Sentence-pair Classification 90.3 # 4
Structured Prediction 81.7 # 5
Question Answering 76.3 # 2
Sentence Retrieval 93.7 # 5
Avg 84.5 # 4


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