spans. We see that CorefNQG still outperforms the baseline models across all metrics.

Figure \[fig:example\] provides sample output for input sentences that require contextual coreference knowledge. We see that ContextNQG fails in all cases; our model misses only the third example due to an error introduced by coreference resolution — the “city” and “it” are considered coreferent. We can also see that human-generated questions are more natural and varied in form with better paraphrasing.

In Table \[tab:tagging\], we show the evaluation results for different answer extraction models. First we see that all variants of BiLSTM models outperform the off-the-shelf NER system (that proposes all NEs as answer spans), though the NER system has a higher recall. The BiLSTM-CRF that encodes the character-level and NER features for each token performs best in terms of F-measure.

**Input 1**: The elizabethan navigator, sir francis drake was born in the nearby town of tavistock and was the mayor of plymouth. ... . .

**Human**: In what year did Sir Francis Drake die ?

**ContextNQG**: When did he die ?

**CorefNQG**: When did sir francis drake die ?

**Input 2**: american idol is an american singing competition ... .

**Human**: When did american idol first 