  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.
