XLM-R Fine-Tuning with Intermediate English Tasks: Zero-Shot Cross-Lingual Consistency

Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks. English dev accuracy is often uncorrelate

Research goal: How does varying the number of intermediate English tasks in fine-tuning affect XLM-R's zero-shot cross-lingual performance on XTREME-R benchmarks when measured by logical consistency scores across different language families?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.