Q = \operatorname*{arg\,max}_{Q} P \left( Q \vert S, C \right)$$ where $P(Q|S,C) = \prod_{n=1}^{N} P \left(y_n \vert y_{<n}, S,C \right)$ where $C$ is the set of sentences that precede $S$ in the paragraph.

Methodology
===========

In this section, we introduce our framework for harvesting the question-answer pairs. As described above, it consists of the question generator CorefNQG (Figure \[fig:nn\]) and a candidate answer extraction module. During test/generation time, we (1) run the answer extraction module on the input text to obtain answers, and then (2) run the question generation module to obtain the corresponding questions.

![image](./figures/coref-nn.pdf)

  -------------------- --------- --------- ---------- ---------- --------- --------- ----------- ----- ----- ----- ----- --
  [ word]{}              they       the     panthers   defeated     the     arizona   cardinals   49     –    15    ...  
  [ans. feature]{}        `O`       `O`       `O`        `O`      `B_ANS`   `I_ANS`    `I_ANS`    `O`   `O`   `O`   ...  
  [coref. feature]{}    `B_PRO`   `B_ANT`   `I_ANT`      `O`        `O`       `O`        `O`      `O`   `O`   `O`   ...  
  -------------------- --------- --------- ---------- ---------- --------- --------- ----------- ----- ----- ----- ----- --

\[tab:fea\_example\]

Question Generation
-------------------

As shown in Figure \[fig:nn\], our generator prepares the feature-rich input embedding — a concatenation of (a) a refined coreference position feature embedding, (b) an answer feature embedding, and (c) a word embedding, each of 