XGLUE
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation.  The training data of each task is in English while the validation and test data is present in multiple different languages. The following table shows which languages are present as validation and test data for each config.
Microsoft
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
part of speech tagging, question answering, natural language inference
nan
Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou,Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti,Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zho

In this paper, we introduce XGLUE, a new
benchmark dataset that can be used to train
large-scale cross-lingual pre-trained models
using multilingual and bilingual corpora and
evaluate their performance across a diverse set
of cross-lingual tasks. Comparing to GLUE
(Wang et al., 2019), which is labeled in English for natural language understanding tasks
only, XGLUE has two main advantages: (1)
it provides 11 diversified tasks that cover both
natural language understanding and generation
scenarios; (2) for each task, it provides labeled
data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder
(Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on
XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual
BERT, XLM and XLM-R for comparison