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 ConvE, only $v_h$ and $v_r$ are reshaped and then concatenated into an input matrix which is fed to the convolution layer 
 Question: Did the authors try stacking multiple convolutional layers?

A: Yes-no
****
Q: Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 . 
 Question: Which embeddings do they detect biases in?

A: Abstractive
****
Q: Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term “fast” pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term “fast” refers to. 
 Question: How does car speak pertains to a car's physical attributes?

A:
Extractive
****