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:
Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?

Ex Output:
tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahçe


Ex Input:
 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?

Ex Output:
5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact


Ex Input:
We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points. 
 Question: How much improvement does jointly learning QA and QG give, compared to only training QA?

Ex Output:

We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points.