Scaling Model Size and Transfer Benefits in Zero-Shot Cross-Lingual Performance

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

How does the scaling of model size (e.g., 7B vs. 13B vs. 30B parameters) affect the transfer benefits of intermediate-task training on zero-shot cross-lingual performance in XTREME-R?

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