Comparison of Inference Efficiency in Cross-Lingual Models Trained with Varying Intermediate-Task Sequences on XGLUE

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 inference efficiency of models trained with different intermediate-task sequences compare when benchmarked on the XGLUE dataset for cross-lingual natural language understanding tasks?

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