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 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 A]: Yes-no

[EX Q]: The output of a system with the target words in the predicted order is compared to the gold ranking of the DURel data set. As the metric to assess how well the model's output fits the gold ranking Spearman's $
ho $ was used. The higher Spearman's rank-order correlation the better the system's performance.
Question: How is evaluation performed?
[EX A]: Extractive

[EX Q]: For the sake of interpretability, we will also project back coefficients from the embeddings as well as PCA models into the dummy variable space.
Question: How do their interpret the coefficients?
[EX A]:
Abstractive