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 Q]: We consider pre-training on the audio data (without labels) of WSJ, part of clean Librispeech (about 80h) and full Librispeech as well as a combination of all datasets (§ SECREF7 ).  Our experimental results on the WSJ benchmark demonstrate that pre-trained representations estimated on about 1,000 hours of unlabeled speech can substantially improve a character-based ASR system and outperform the best character-based result in the literature, Deep Speech 2.  
 Question: Which unlabeled data do they pretrain with?
[EX A]: Abstractive

[EX Q]: We thus adapt an off-the-shelf reward learning algorithm BIBREF7 to the supervised setting for automated data manipulation. 
 Question: What off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training is adapted?
[EX A]: Extractive

[EX Q]: We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. 
 Question: By how much does their model outperform existing methods?
[EX A]:
Abstractive