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

A: Abstractive
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Q: A causal attribution dataset is a collection of text pairs that reflect cause-effect relationships proposed by humans (for example, "virus causes sickness"). These written statements identify the nodes of the network (see also our graph fusion algorithm for dealing with semantically equivalent statements) while cause-effect relationships form the directed edges ("virus" $
ightarrow $ "sickness") of the causal attribution network.
Question: What are causal attribution networks?

A: Abstractive
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Q: We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus.
Question: What user variations have been tested?

A:
Extractive
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