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.

Ex Input:
We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus.  
 Question: What user variations have been tested?

Ex Output:
completion times and accuracies 


Ex Input:
Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats.

Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer);

Untargeted (UNT): Posts containing non-targeted profanity and swearing. Posts with general profanity are not targeted, but they contain non-acceptable language. Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH).

Individual (IND): Posts targeting an individual. It can be a a famous person, a named individual or an unnamed participant in the conversation. Insults and threats targeted at individuals are often defined as cyberbulling.

Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic. Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech.

Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue). 
 Question: What kinds of offensive content are explored?

Ex Output:
non-targeted profanity and swearing, targeted insults such as cyberbullying, offensive content related to ethnicity, gender or sexual orientation, political affiliation, religious belief, and anything belonging to hate speech


Ex Input:
To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery). 
 Question: Do they beat current state-of-the-art on SICK?

Ex Output:

No