XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation - Liang, Yaobo; Duan, Nan; Gong, Yeyun; Wu, Ning; Guo, Fenfei; Qi, Weizhen; Gong (YIMING), Ming; Shou (寿林钧), Linjun; Jiang (姜大昕), Daxin; Cao, Guihong; Fan, Xiaodong; Zhang, Bruce; et al. (EMNLP 2020, 2020)
In this paper, we introduce XGLUE, a new benchmark dataset 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 and includes natural language understanding tasks only, XGLUE has three main advantages: (1) it provides two corpora with different sizes for cross-lingual pre-training; (2) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (3) 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.