in Figure \[fig:problem\], the second and third questions require coreference information (i.e., recognizing that “His” in sentence 2 and “He” in sentence 3 both corefer with “Tesla” in sentence 1) to answer them.

Thus, our research studies methods for incorporating coreference information into the training of a question generation system. In particular, we propose gated [**Coref**]{}erence knowledge for [**N**]{}eural [**Q**]{}uestion [**G**]{}eneration ([**CorefNQG**]{}), a neural sequence model with a novel gating mechanism that leverages continuous representations of [*coreference clusters*]{} — the set of mentions used to refer to each entity — to better encode linguistic knowledge introduced by coreference, for paragraph-level question generation.

In an evaluation using the SQuAD dataset, we find that CorefNQG enables better question generation. It outperforms significantly the baseline neural sequence models that encode information from a single sentence, and a model that encodes *all* preceding context and the input sentence itself. When evaluated on only the portion of SQuAD that requires coreference resolution, the gap between our system and the baseline systems is even larger.

By applying our approach to the 10,000 top-ranking Wikipedia articles, we obtain a question answering/reading comprehension dataset with over one million QA pairs; we provide a qualitative analysis in Section \[sec:res\]. 