Optimal Intermediate Task Quantity for Zero-Shot Cross-Lingual Transfer Accuracy in XTREME-R

Abstract

Abstract: Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot s

Research Question

Does increasing the number of intermediate tasks beyond a certain threshold (e.g., 9 tasks as in the paper) lead to diminishing returns in zero-shot cross-lingual transfer accuracy on XTREME-R, and if so, what is the optimal number for cost-efficient performance?

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