We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.

Unicoder是一种机器学习算法，可对自然语言进行编码，进而实现输出不同语言的任务。该方法提出了三种新的跨语言预训练任务，包括跨语言词汇恢复、跨语言近义词分类和跨语言掩码语言模型。作者还发现在多种语言上进行微调可以进一步提高性能。作者在两个任务上进行了实验，包括跨语言自然语言推断和跨语言问答，并且在新的跨语言问答数据集上实现了5.5％的精确度提高。

Unicoder：通过多语种交叉训练预训练的通用语言编码器