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.

We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance.  On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100.  
 Question: How are the two different models trained?
Abstractive

We deployed the task on Amazon Mechanical Turk (AMT). To see how reasoning varies across workers, we hire 3 crowdworkers per one instance. We hire reliable crowdworkers with $\ge 5,000$ HITs experiences and an approval rate of $\ge $ 99.0%, and pay ¢20 as a reward per instance. 
 Question: Did they use any crowdsourcing platform?
Yes-no

Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: Was the approach used in this work to detect fake news fully supervised?

Yes-no