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.

Q: 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?

A: Yes-no
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Q: Multi-task Pairwise Neural Ranking
We propose a multi-task pairwise neural ranking approach to better incorporate and distinguish the relative order between the candidate segmentations of a given hashtag. Our model adapts to address single- and multi-token hashtags differently via a multi-task learning strategy without requiring additional annotations. In this section, we describe the task setup and three variants of pairwise neural ranking models (Figure FIGREF11 ). Pairwise Neural Ranking Model Margin Ranking (MR) Loss Adaptive Multi-task Learning 
 Question: What set of approaches to hashtag segmentation are proposed?

A: Extractive
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Q: We measure how contextual a word representation is using three different metrics: self-similarity, intra-sentence similarity, and maximum explainable variance. 
 Question: What experiments are proposed to test that upper layers produce context-specific embeddings?

A:
Abstractive
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