Performance Comparison of Monolingual and Multilingual Intermediate-Task Fine-Tuning for Zero-Shot Cross-Lingual Transfer

Abstract

Abstract: 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 Question

How does the performance of monolingual English intermediate-task fine-tuning compare to multilingual intermediate-task fine-tuning (e.g., using XLM-R) on zero-shot cross-lingual transfer for languages with varying script similarity to English, as measured by F1/F1-avg scores on XNLI and TYDI-QA benchmarks?

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