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.

Example Input: The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next. 
 Question: Do they literally just treat this as "predict the next spell that appears in the text"?
Example Output: Yes-no

Example Input: In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel). 
 Question: Is the performance compared against a baseline model?
Example Output: Yes-no

Example Input: We present a comparison of the proposed models to existing word embeddings approaches. These are: the Bernoulli embeddings (b-emb) BIBREF1 , continuous bag-of-words (CBOW) BIBREF5 , Distributed Memory version of Paragraph Vector (PV-DM) BIBREF11 and the Global Vectors (GloVe) BIBREF6 model. 
 Question: What word embeddings do they test?
Example Output:
Extractive