Both human and machine translation play a central role in cross-lingual
transfer learning: many multilingual datasets have been created through
professional translation services, and using machine translation to translate
either the test set or the training set is a widely used transfer technique. In
this paper, we show that such translation process can introduce subtle
artifacts that have a notable impact in existing cross-lingual models. For
instance, in natural language inference, translating the premise and the
hypothesis independently can reduce the lexical overlap between them, which
current models are highly sensitive to. We show that some previous findings in
cross-lingual transfer learning need to be reconsidered in the light of this
phenomenon. Based on the gained insights, we also improve the state-of-the-art
in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points,
respectively.

研究报告发现人工和机器翻译在跨语言学习中的影响，翻译过程会引入细微的错误，影响到跨语言模型的准确性。在此基础上，对 XNLI 测试和零样本学习进行改进，取得了 4.3 和 2.8 个百分点的提升。