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: We test the character and word-level variants by predicting hashtags for a held-out test set of posts. 
 Question: What other tasks do they test their method on?
Example Output: Abstractive

Example Input: In this section, we propose new simple disentanglement models that perform better than prior methods, and re-examine prior work. 
 Question: Did they experiment with the corpus?
Example Output: Yes-no

Example Input: The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence.  The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.  
 Question: What was the baseline for this task?
Example Output:
Abstractive