  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 setting.
