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.

We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc.  
 Question: Do the tweets come from any individual?
Yes

In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier. 
 Question: What are the components of the multilingual framework?
text-transformations to the messages vector space model Support Vector Machine

For training and testing we use the train-validation-test split of WikiTableQuestions BIBREF0 , a dataset containing 22,033 pairs of questions and answers based on 2,108 Wikipedia tables. This dataset is also used by our baselines, BIBREF0 , BIBREF3 . 
 Question: Does the dataset they use differ from the one used by Pasupat and Liang, 2015?

No