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:
We cluster the embeddings with INLINEFORM0 -Means.  
 Question: How were the cluster extracted? 

Ex Output:
Abstractive


Ex Input:
The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2.  
 Question: Do they use dropout?

Ex Output:
Yes-no


Ex Input:
We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance.  On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100.  
 Question: How are the two different models trained?

Ex Output:

Abstractive