  Substantial improvements have been made in machine reading comprehension,
where the machine answers questions based on a given context. Current
state-of-the-art models even surpass human performance on several benchmarks.
However, their abilities in the cross-lingual scenario are still to be
explored. Previous works have revealed the abilities of pre-trained
multilingual models for zero-shot cross-lingual reading comprehension. In this
paper, we further utilized unlabeled data to improve the performance. The model
is first supervised-trained on source language corpus, and then self-trained
with unlabeled target language data. The experiment results showed improvements
for all languages, and we also analyzed how self-training benefits
cross-lingual reading comprehension in qualitative aspects.
