TASK DEFINITION: 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.
PROBLEM: As a result, transfer training with only 1000 hours data can match equivalent performance for full training with 7300 hours data. 
 Question: how small of a dataset did they train on?

SOLUTION: Extractive

PROBLEM: Each dataset consists of a collection of records with one QA problem per record. For each record, we include some question text, a context document relevant to the question, a set of candidate solutions, and the correct solution. The context document for each record consists of a list of ranked and scored pseudodocuments relevant to the question. Several baselines rely on the retrieved context to extract the answer to a question. For these, we refer to the fraction of instances for which the correct answer is present in the context as Search Accuracy. Naturally, the search accuracy increases as the context size increases, however at the same time reading performance decreases since the task of extracting the answer becomes harder for longer documents. 
 Question: Which retrieval system was used for baselines?

SOLUTION: Abstractive

PROBLEM: 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?

SOLUTION:
Extractive