work? ]{}\
[ ** ]{}\
[ – Where did Tesla live for much of his life? ]{}\
[ ** ]{}\

Thus, there is a need for methods that can automatically generate high-quality question-answer pairs.  propose the use of recurrent neural networks to generate QA pairs from structured knowledge resources such as Freebase. Their work relies on the existence of automatically acquired KBs, which are known to have errors and suffer from incompleteness. They are also non-trivial to obtain. In addition, the questions in the resulting dataset are limited to queries regarding a single fact (i.e., tuple) in the KB.

Motivated by the need for large scale QA pairs and the limitations of recent work, we investigate methods that can automatically “harvest” (generate) question-answer pairs from raw text/unstructured documents, such as Wikipedia-type articles.

Recent work along these lines [@du2017LearningToAsk; @zhou2017neural] (see Section \[sec:related\]) has proposed the use of attention-based recurrent neural models trained on the crowdsourced SQuAD dataset [@rajpurkar2016squad] for question generation. While successful, the resulting QA pairs are based on information from a single sentence. As described in , however, nearly 30% of the questions in the human-generated questions of SQuAD rely on information beyond a single sentence. For example, 