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:  To remain consistent with experiments performed with LSTM's we use the morfessor for the subword tokenization in the Finnish Language. 
 Question: Is the LSTM baseline a sub-word model?
Example Output: Yes-no

Example Input: The baselines are: (a) trained on S-SQuAD, (b) trained on T-SQuAD and then fine-tuned on S-SQuAD, and (c) previous best model trained on S-SQuAD BIBREF5 by using Dr.QA BIBREF20 . The proposed approach (row (f)) outperforms previous best model (row (c)) by 2% EM score and over 1.5% F1 score. 
 Question: What was the previous best model?
Example Output: Extractive

Example Input: BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization.  
 Question: What were the non-neural baselines used for the task?
Example Output:
Abstractive