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.

Example Input: In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation. Experimental results show that the encoder-decoder-reconstructor offers significant improvement in BLEU scores and alleviates the problem of repeating and missing words in the translation on English-Japanese translation task, and the encoder-decoder-reconstructor can not be trained well without pre-training, so it proves that we have to train the forward translation model in a manner similar to the conventional attention-based NMT as pre-training. 
 Question: Is pre-training effective in their evaluation?
Example Output: Yes-no

Example Input: The average classification accuracy results are summarised in Table TABREF9. 
 Question: What evaluation metric is used?
Example Output: Extractive

Example Input: Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?
Example Output:
Abstractive