  Automatic question generation (QG) is a challenging problem in natural
language understanding. QG systems are typically built assuming access to a
large number of training instances where each instance is a question and its
corresponding answer. For a new language, such training instances are hard to
obtain making the QG problem even more challenging. Using this as our
motivation, we study the reuse of an available large QG dataset in a secondary
language (e.g. English) to learn a QG model for a primary language (e.g. Hindi)
of interest. For the primary language, we assume access to a large amount of
monolingual text but only a small QG dataset. We propose a cross-lingual QG
model which uses the following training regime: (i) Unsupervised pretraining of
language models in both primary and secondary languages and (ii) joint
supervised training for QG in both languages. We demonstrate the efficacy of
our proposed approach using two different primary languages, Hindi and Chinese.
We also create and release a new question answering dataset for Hindi
consisting of 6555 sentences.
