Scaling Intermediate Language Tasks for Zero-Shot Cross-Lingual Performance on XTREME-R

Zero-shot cross-lingual knowledge transfer enables the multilingual pretrained language model (mPLM), finetuned on a task in one language, make predictions for this task in other languages. While being broadly studied for natural language understanding tasks, the described setting is understudied for generation. Previous works notice a frequent problem of generation in a wrong language and propose approaches to address it, usually using mT5 as a backbone model. In this work, we test alternative mPLMs, such as mBART and NLLB-200, considering full finetuning and parameter-efficient finetuning wi

Research goal: What is the impact of scaling the number of intermediate language-understanding tasks on the zero-shot cross-lingual performance of models on XTREME-R?

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