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: We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively.
Question: What profile metadata is used for this analysis?

A: Extractive
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Q: If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model.
Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

A: Extractive
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Q: The final code-mixed tweets were forwarded to a group of three annotators who were university students and fluent in both English and Hindi.
Question: How many annotators tagged each text?

A:
Extractive
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