that it contains. For the question generation system, we pair each sentence that contains an answer span with the corresponding gold question as in .

To quantify the effect of using predicted (rather than gold standard) answer spans on question generation (e.g., predicted answer span boundaries can be inaccurate), we also train the models on an augmented “Training set w/ noisy examples” (see Table \[tab:qg\]). This training set contains all of the original training examples [*plus*]{} new examples for predicted answer spans (from the top-performing answer extraction model, bottom row of Table \[tab:tagging\]) that [*overlap*]{} with a gold answer span. We pair the new training sentence (w/ predicted answer span) with the gold question. The added examples comprise 42.21% of the noisy example training set. For generation of our one million QA pair corpus, we apply our systems to the 10,000 top-ranking articles of Wikipedia.

Evaluation Metrics
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For question generation evaluation, we use BLEU [@papineni2002bleu] and METEOR [@2014meteor].[^1] BLEU measures average $n$-gram precision vs. a set of reference questions and penalizes for overly short sentences. METEOR is a recall-oriented metric that takes into account synonyms, stemming, and paraphrases.

For answer candidate extraction evaluation, we use precision, recall and F-measure 