  Deep learning based question answering (QA) on English documents has achieved
success because there is a large amount of English training examples. However,
for most languages, training examples for high-quality QA models are not
available. In this paper, we explore the problem of cross-lingual transfer
learning for QA, where a source language task with plentiful annotations is
utilized to improve the performance of a QA model on a target language task
with limited available annotations. We examine two different approaches. A
machine translation (MT) based approach translates the source language into the
target language, or vice versa. Although the MT-based approach brings
improvement, it assumes the availability of a sentence-level translation
system. A GAN-based approach incorporates a language discriminator to learn
language-universal feature representations, and consequentially transfer
knowledge from the source language. The GAN-based approach rivals the
performance of the MT-based approach with fewer linguistic resources. Applying
both approaches simultaneously yield the best results. We use two English
benchmark datasets, SQuAD and NewsQA, as source language data, and show
significant improvements over a number of established baselines on a Chinese QA
task. We achieve the new state-of-the-art on the Chinese QA dataset.
