instruction:
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.
question:
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?
answer:
Extractive


question:
The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest...

On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory! 
 Question: Do they break down word meanings into elementary particles as in the standard model of quantum theory?
answer:
Yes-no


question:
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC).  
 Question: What approaches without reinforcement learning have been tried?
answer:

Abstractive