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: Visual Question Generation (VQG) is another emerging topic which aims to ask questions given an image. Motivated by this, BIBREF10 proposed open-ended VQG that aims to generate natural and engaging questions about an image. 
 Question: Do they survey visual question generation work?

A: Yes-no
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Q: We address this tension by training vector space models to represent the data, in which each unique word in a large corpus is represented by a vector (embedding) in high-dimensional space. The geometry of the resulting vector space captures many semantic relations between words.  We address this tension by training vector space models to represent the data, in which each unique word in a large corpus is represented by a vector (embedding) in high-dimensional space. The geometry of the resulting vector space captures many semantic relations between words.  
 Question: Do they model semantics 

A: Yes-no
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Q: We present LOG-Cad, a neural network-based description generator (Figure FIGREF1 ) to directly solve this task. Given a word with its context, our generator takes advantage of the target word's embedding, pre-trained from massive text (global contexts), while also encoding the given local context, combining both to generate a natural language description. 
 Question: Do they use pretrained word embeddings?

A:
Yes-no
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