instruction:
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.
question:
We now seek to know if a pre-trained multi-BERT has ability to solve RC tasks in the zero-shot setting. 
 Question: What model is used as a baseline?  
answer:
Extractive


question:
In this paper we focus on the skipgram approach with random negative examples proposed in BIBREF0 .  
 Question: Do they use skipgram version of word2vec?
answer:
Yes-no


question:
For each variable in encoding set, learn the new embeddings using the embeddings train set . Since there is stochasticity in the embeddings training model, we will repeat the above multiple times, for the different experiments in the paper (and report the respective mean and standard deviation statistics). 
 Question: How do their train their embeddings?
answer:

Abstractive