In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

[Q]: A growing body of evidence shows that state-of-the-art models learn to exploit spurious statistical patterns in datasets BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, instead of learning meaning in the flexible and generalizable way that humans do. Given this, human annotators—be they seasoned NLP researchers or non-experts—might easily be able to construct examples that expose model brittleness. 
 Question: What are the weaknesses found by non-expert annotators of current state-of-the-art NLI models?
[A]: Extractive


[Q]: We call the model as a modified Toulmin's model. It contains five argument components, namely claim, premise, backing, rebuttal, and refutation. When annotating a document, any arbitrary token span can be labeled with an argument component; the components do not overlap.  
 Question: What argument components do the ML methods aim to identify?
[A]: Extractive


[Q]: Compared to single-task baselines, performance improved on the low-resource En-De task and was comparable on high-resource En-Fr task. 
 Question: How big are negative effects of proposed techniques on high-resource tasks?
[A]:
Abstractive