In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[EX Q]: In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.
[EX A]: Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?

[EX Q]: Our definition of bias is now: [Interpretive Bias] An interpretive bias in an epistemic ME game is the probability distribution over types given by the belief function of the conversationalists or players, or the Jury. Note that in an ME game there are typically several interpretive biases at work: each player has her own bias, as does the Jury.
[EX A]: Which interpretative biases are analyzed in this paper?

[EX Q]: Posts in FBFans dataset are used for this analysis. We calculate the like statistics of each distinct author from these 32,595 posts.
[EX A]:
What is the size of the Chinese data?