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]: 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?
[EX A]: Yes-no

[EX Q]: Our Recurrent +ELMo model uses the language model from BIBREF9 to provide contextualized embeddings to the baseline model outlined above, as recommended by the authors.

Our OpenAI GPT model fine-tunes the 12 layer 768 dimensional uni-directional transformer from BIBREF27 , which has been pre-trained as a language model on the Books corpus BIBREF36 . 
 Question: did they use other pretrained language models besides bert?
[EX A]: Yes-no

[EX Q]: Further, we employ BLEU score to evaluate the accuracy of generations BIBREF21, and the number of distinct n-gram to evaluate the diversity of generations BIBREF6.  
 Question: How do they measure the diversity of inferences?
[EX A]:
Abstractive