Daily Mail news articles with their summarized bullet points.  showed that this dataset is quite noisy due to the method of data creation and concluded that performance of QA systems on the dataset is almost saturated.

Closest to our work is that of . They train a neural triple-to-sequence model on SimpleQuestions, and apply their system to Freebase to produce a large collection of human-like question-answer pairs.

Task Definition {#sec:ans}
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Our goal is to harvest high quality question-answer pairs from the paragraphs of an article of interest. In our task formulation, this consists of two steps: [**candidate answer extraction**]{} and [**answer-specific question generation**]{}. Given an input paragraph, we first identify a set of [*question-worthy*]{} candidate answers $ans = (ans_1, ans_2, ..., ans_l)$, each a span of text as denoted in color in Figure \[fig:problem\]. For each candidate answer $ans_i$, we then aim to generate a question $Q$ — a sequence of tokens $y_1,..., y_N$ — based on the sentence $S$ that contains candidate $ans_i$ such that:

-   $Q$ asks about an aspect of $ans_i$ that is of potential interest to a human;

-   $Q$ might rely on information from sentences that precede $S$ in the paragraph.

Mathematically then, $$\label{equ:task}
