Impact of Intermediate Task Sequencing on Multilingual Fine-Tuning Robustness and Accuracy

Abstract

Abstract: Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks. Previously, for zero-shot cross-lingual evaluation, pre-trained models are only fine-tuned on English data and tested on a variety of target languages. In this paper, we do cross-lingual evaluation on various NLU tasks (sentence classification, sequence labeling, question answering) using prompt-tuning and compare it with fine-tuning. The results show that prompt tuning achieves much better cross-lingual transfer t

Research Question

Does the sequence of intermediate tasks in multilingual fine-tuning pipelines affect robustness and accuracy across diverse language families in cross-lingual NLU evaluations?

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