In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: We opted for an automatic approach instead, that can be scaled arbitrarily and at little cost: we generate synthetic sentence pairs $(, ilde{})$ by randomly perturbing 1.8 million segments $$ from Wikipedia. We use three techniques: mask-filling with BERT, backtranslation, and randomly dropping out words.
Question: How are the synthetic examples generated?

A: Random perturbation of Wikipedia sentences using mask-filling with BERT, backtranslation and randomly drop out
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Q: Impact votes are provided by the users of the platform to evaluate how impactful a particular claim is. Users can pick one of 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact. While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument.
Question: What annotations are available in the dataset?

A: 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact
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Q: For the sake of simplicity, we focus our analysis on Airbnb listings from Manhattan, NY, during the time period of January 1, 2016, to January 1, 2017. The data provided to us contained information for roughly 40,000 Manhattan listings that were posted on Airbnb during this defined time period. For each listing, we were given information of the amenities of the listing (number of bathrooms, number of bedrooms ), the listing's zip code, the host's description of the listing, the price of the listing, and the occupancy rate of the listing.
Question: What is the size of the Airbnb?

A:
roughly 40,000 Manhattan listings
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