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.

Input: Consider Input: Our baseline is TransE since that the score function of our models is based on TransE. 
 Question: What baselines are used for comparison?

Output: Extractive


Input: Consider Input: Thus we consider the task of answer generation for TweetQA and we use several standard metrics for natural language generation to evaluate QA systems on our dataset, namely we consider BLEU-1 BIBREF16 , Meteor BIBREF17 and Rouge-L BIBREF18 in this paper. 
 Question: What evaluation metrics do they use?

Output: Extractive


Input: Consider Input: Subsequently, an LSTM decoder generates the characters in the output word form using encoder states and an attention mechanism. 
 Question: Does the model have attention?


Output: Yes-no