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: Tourist: I can't go straight any further.

Guide: ok. turn so that the theater is on your right.

Guide: then go straight

Tourist: That would be going back the way I came

Guide: yeah. I was looking at the wrong bank

Tourist: I'll notify when I am back at the brooks brothers, and the bank.

Tourist: ACTION:TURNRIGHT

Guide: make a right when the bank is on your left

Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT

Tourist: Making the right at the bank.

Tourist: ACTION:FORWARD ACTION:FORWARD

Tourist: I can't go that way.

Tourist: ACTION:TURNLEFT

Tourist: Bank is ahead of me on the right

Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT

Guide: turn around on that intersection

Tourist: I can only go to the left or back the way I just came.

Tourist: ACTION:TURNLEFT

Guide: you're in the right place. do you see shops on the corners?

Guide: If you're on the corner with the bank, cross the street

Tourist: I'm back where I started by the shop and the bank.

Tourist: ACTION:TURNRIGHT 
 Question: What language do the agents talk in?

Output: Abstractive


Input: Consider Input: In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. 
 Question: what ml based approaches were compared?

Output: Extractive


Input: Consider Input: Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method. By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out. 
 Question: Do they report results only on English data?


Output: Yes-no