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.

[EX Q]: Therefore, we decided to compile our own corpora based on English documents, which we crawled from different publicly accessible sources. 
 Question: Do they report results only on English data?
[EX A]: Yes-no

[EX Q]: The decoder is the second LSTM network that uses the information obtained from the encoder to generate the sequence's story. The first input $x_0$ to the decoder is the image for which the text is being generated. The last hidden state from the encoder $h_e^{(t)}$ is used to initialize the first hidden state of the decoder $h_d^{(0)}$ . With this strategy, we provide the decoder with the context of the whole sequence and the content of the current image (i.e. global and local information) to generate the corresponding text that will contribute to the overall story. 
 Question: How is the sequential nature of the story captured?
[EX A]: Extractive

[EX Q]: The NLG model is a seq2seq model with attention as described in section SECREF2. 
 Question: Do they use attention?
[EX A]:
Yes-no