answer span extraction, we conduct experiments to compare the performance of an off-the-shelf NER system and BiLSTM based systems. For [**training and implementation details**]{}, please see the Supplementary Material.

Results and Analysis {#sec:res}
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Automatic Evaluation
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Table \[tab:qg\] shows the BLEU-$\{3,4\}$ and METEOR scores of different models. Our CorefNQG outperforms the seq2seq baseline of  by a large margin. This shows that the copy mechanism, answer features and coreference resolution all aid question generation. In addition, CorefNQG outperforms both Seq2seq+Copy models significantly, whether or not they have access to the full context. This demonstrates that the coreference knowledge encoded with the gating network explicitly helps with the training and generation: it is more difficult for the neural sequence model to learn the coreference knowledge in a latent way. (See input 1 in Figure \[fig:example\] for an example.) Building end-to-end models that take into account coreference knowledge in a latent way is an interesting direction to explore. In the ablation tests, the performance drop of CorefNQG–gating shows that the gating network is playing an important role for getting [*refined*]{} coreference position feature embedding, which helps the model learn the importance of an antecedent. The performance drop of CorefNQG–mention-pair score shows the 