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:
As a result, transfer training with only 1000 hours data can match equivalent performance for full training with 7300 hours data. 
 Question: how small of a dataset did they train on?

Ex Output:
Extractive


Ex Input:
Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204. 
 Question: Do they use the same distance metric for both the SimCluster and K-means algorithm?

Ex Output:
Yes-no


Ex Input:
For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length. 
 Question: Do they show an example of usage for INFODENS?

Ex Output:

Yes-no