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 Input:
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. During annotation, we generally relied on categories and guidelines assembled by BBN Technologies for TREC 2002 question answering track. 
 Question: did they use a crowdsourcing platform for manual annotations?

Ex Output:
Yes-no


Ex Input:
In this study we use transcripts and results of Oxford-style debates from the public debate series “Intelligence Squared Debates” (IQ2 for short). 
 Question: what debates dataset was used?

Ex Output:
Extractive


Ex Input:
The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks. 
 Question: Does the new objective perform better than the original objective bert is trained on?

Ex Output:

Yes-no