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 Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 . 
 Question: What was their performance on emotion detection?

A: Abstractive
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Q: d  Experiments on two benchmark data sets, the Stanford Sentiment Treebank BIBREF7 and the AG English news corpus BIBREF3 , show that 1) our method achieves very competitive accuracy, 2) some views distinguish themselves from others by better categorizing specific classes, and 3) when our base bag-of-words feature set is augmented with convolutional features, the method establishes a new state-of-the-art for both data sets.  Stanford Sentiment Treebank  AG English news corpus  
 Question: which benchmark tasks did they experiment on?

A: Abstractive
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Q: We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task. 
 Question: Was any extrinsic evaluation carried out?

A:
Yes-no
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