XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
This provides a standardized evaluation framework for cross-lingual AI models, addressing the need for multilingual benchmarks in NLP, but it is incremental as it builds upon existing datasets like GLUE.
The authors introduced XGLUE, a benchmark dataset for cross-lingual pre-training, understanding, and generation, offering 11 tasks in multiple languages, and evaluated models like Unicoder, Multilingual BERT, XLM, and XLM-R as baselines.
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.