the full context.

Table \[tab:human\] shows the results of the human evaluation. Bold indicates top scores. We see that the original human questions are preferred over the two NQG systems’ outputs, which is understandable given the examples in Figure \[fig:example\]. The human-generated questions make more sense and correspond better with the provided answers, particularly when they require information in the preceding context. How exactly to capture the preceding context so as to *ask* better and more diverse questions is an interesting future direction for research. In terms of grammaticality, however, the neural models do quite well, achieving very close to human performance. In addition, we see that our method (CorefNQG) performs statistically significantly better across all metrics in comparison to the baseline model (ContextNQG), which has access to the entire preceding context in the passage.

The Generated Corpus {#sec:dataset}
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Our system generates in total 1,259,691 question-answer pairs, nearly 126 questions per article. Figure \[fig:distr\] shows the distribution of different types of questions in our dataset vs. the SQuAD training set. We see that the distribution for “In what”, “When”, “How long”, “Who”, “Where”, “What does” and “What do” questions in the two datasets is similar. Our system generates more 