Name: XGLUE
Description: 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.
Provider: Microsoft
Paper Title: XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Tasks: part of speech tagging, question answering, natural language inference
Venue Name: nan
Authors: 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
Abstract: 
  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