The answer extractor identifies the answer span boundary well and all three questions correspond to their answers. Q2 is valid but not entirely accurate. For more examples, please refer to our supplementary materials.

Table \[tab:performance\] shows the performance of a top-performing system for the SQuAD dataset (Document Reader [@chen2017reading]) when applied to the development and test set portions of our generated dataset. The system was trained on the training set portion of our dataset. We use the SQuAD evaluation scripts, which calculate exact match (EM) and F-1 scores.[^2] Performance of the neural machine reading model is reasonable. We also train the DocReader on our training set and test the models’ performance on the *original* dev set of SQuAD; for this, the performance is around $45.2\%$ on EM and $56.7\%$ on F-1 metric. DocReader trained on the *original* SQuAD training set achieves $69.5\%$ EM, $78.8\%$ F-1 indicating that our dataset is more difficult and/or less natural than the crowd-sourced QA pairs of SQuAD.

Conclusion
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We propose a new neural network model for better encoding coreference knowledge for paragraph-level question generation. Evaluations with different metrics on the SQuAD machine reading dataset show that our model outperforms state-of-the-art baselines. The ablation study 