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 Input:
In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers.

Ex Output:
Which framework they propose in this paper?


Ex Input:
Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories.

Ex Output:
What are the weaknesses of their proposed interpretability quantification method?


Ex Input:
 In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks BIBREF53 , BIBREF54 

Ex Output:

What is the current SOTA for sentiment analysis on Twitter at the time of writing?