An automatic question generation (QG) system aims to produce questions from a text,
such as a sentence or a paragraph. This system can be useful on the frontline of education,
as making questions is a time-consuming and expert-participating craft. Traditional
approaches are mainly based on heuristic and hand-crafted rules to transduce a
declarative sentence into a related interrogative sentence. In this work, we propose a
data-driven approach, which leverages a neural sequence-to-sequence framework with
various transfer learning strategies to capture the underlying information of making a
question, on a target domain with rare training pairs. Our experiment shows this
modified model is capable to generate satisfactory results to some extent.

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