Performance Comparison of Multilingual Models on XGLUE NLP Tasks Using BLEU and ROUGE Scores

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

Research goal: How does the performance of multilingual models fine-tuned on XGLUE's natural language generation tasks compare to their performance on natural language understanding tasks when evaluated using BLEU and ROUGE scores?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.