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 Input:
We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 .
Question: Do they provide a framework for building a sub-differentiable for any final loss metric?

Ex Output:
Yes-no

Ex Input:
TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?

Ex Output:
Yes-no

Ex Input:
From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts. Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers. Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently.
Question: What are the characteristics of the accounts that spread fake news?

Ex Output:
Abstractive