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XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

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arxiv 2004.01401 v3 pith:6PE3KLUB submitted 2020-04-03 cs.CL

Yaobo Liang , Nan Duan , Yeyun Gong , Ning Wu , Fenfei Guo , Weizhen Qi , Ming Gong , Linjun Shou
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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 Zhou
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classification cs.CL
keywords cross-lingualtasksunderstandingxgluegenerationbenchmarkcoverdataset
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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.

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Cited by 7 Pith papers

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