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]: This allows Macaw to support multi-modal interactions, such as text, speech, image, click, etc. 
 Question: What modalities are supported by Macaw?
[A]: Extractive


[Q]: In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning. 
 Question: How does their model differ from BERT?
[A]: Abstractive


[Q]: Looking to other target tasks, the grammar-related CoLA task benefits dramatically from ELMo pretraining: The best result without language model pretraining is less than half the result achieved with such pretraining. In contrast, the meaning-oriented textual similarity benchmark STS sees good results with several kinds of pretraining, but does not benefit substantially from the use of ELMo. 
 Question: Do some pretraining objectives perform better than others for sentence level understanding tasks?
[A]:
Yes-no