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]: We test the existing debiasing approaches, CDA and REG, as well but since BIBREF5 reported that results fluctuate substantially with different REG regularization coefficients, we perform hyperparameter tuning and report the best results in Table TABREF12 . 
 Question: which existing strategies are compared?
[EX A]: Extractive

[EX Q]: Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech.  
 Question: How many tweets are in the dataset?
[EX A]: Extractive

[EX Q]: We also evaluate all five models on downstream tasks from the VecEval suite BIBREF13 , using only the tasks for which training and evaluation data is freely available: chunking, sentiment and question classification, and natural language identification (NLI). The default settings from the suite are used, but we run only the fixed settings, where the embeddings themselves are not tunable parameters of the models, forcing the system to use only the information already in the embeddings.

Finally, we use LV-N, LV-M, and FT to generate OOV word representations for the following words: 1) “hellooo”: a greeting commonly used in instant messaging which emphasizes a syllable. 2) “marvelicious”: a made-up word obtained by merging “marvelous” and “delicious”. 3) “louisana”: a misspelling of the proper name “Louisiana”. 4) “rereread”: recursive use of prefix “re”. 5) “tuzread”: made-up prefix “tuz”. 
 Question: How do they evaluate their resulting word embeddings?
[EX A]:
Extractive