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:
One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21. 
 Question: How does counterfactual data augmentation aim to tackle bias?

Ex Output:
Abstractive


Ex Input:
Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game. 
 Question: What is the average length of the recordings?

Ex Output:
Abstractive


Ex Input:
 On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results.  
 Question: In the proposed metric, how is content relevance measured?

Ex Output:

Abstractive