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]: In this approach the similarity between two words is not strictly based on their co-occurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i.e., second order co-occurrences). This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13 . 
 Question: What is a second order co-ocurrence matrix?
[A]: Extractive


[Q]: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous.  Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . 
 Question: Do they provide a framework for building a sub-differentiable for any final loss metric?
[A]: Yes-no


[Q]: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29. 
 Question: Is this a span-based (extractive) QA task?
[A]:
Yes-no