XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Liang, Yaobo, Duan, Nan, Gong, Yeyun, Wu, Ning, Guo, Fenfei, Qi, Weizhen, Gong, Ming, Shou, Linjun, Jiang, Daxin, Cao, Guihong, Fan, Xiaodong, Zhang, Ruofei, Agrawal, Rahul, Cui, Edward, Wei, Sining, Bharti, Taroon, Qiao, Ying, Chen, Jiun-Hung, Wu, Winnie, Liu, Shuguang, Yang, Fan, Campos, Daniel, Majumder, Rangan, Zhou, Ming
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3014635508