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.

Q: Here I would use Informap algorithm BIBREF12. To make a comparison to this method, I am using CCM and SCA for distance measurement in this experiment, too. UPGMA algorithm would be used accordingly in these two cases.

A: Is the proposed method compared to previous methods?
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Q: 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.

A: Which framework they propose in this paper?
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Q: Table 1 shows an example of difficult and easy examples according to our definition of difficulty. The underlined text demarcates the (consensus) reference label provided by domain experts. In the difficult examples, crowd workers marked text distinct from these reference annotations; whereas in the easy cases they reproduced them with reasonable fidelity. The difficult sentences usually exhibit complicated structure and feature jargon.

A:
Is an instance a sentence or an IE tuple?
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