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: Noticeably, despite being both based on and integrated into a BERT$_\text{base}$ model, our architecture even outperforms a standalone BERT$_\text{large}$ model by a large margin. 
 Question: What models other than standalone BERT is new model compared to?
Example Output: Abstractive

Example Input: Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. 
 Question: What is the source of the "control" corpus?
Example Output: Abstractive

Example Input: We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18. 
 Question: Do they use crowdsourcing to collect human judgements?
Example Output:
Yes-no

