TASK DEFINITION: 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.
PROBLEM: Because of this, we do not claim that this dataset can be considered a ground truth. 
 Question: How is the ground truth for fake news established?

SOLUTION: Ground truth is not established in the paper

PROBLEM: 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?

SOLUTION: text-transformations to the messages vector space model Support Vector Machine

PROBLEM: Given a pair of ultrasound and audio segments we can calculate the distance between them using our model. To predict the synchronisation offset for an utterance, we consider a discretised set of candidate offsets, calculate the average distance for each across utterance segments, and select the one with the minimum average distance.  
 Question: Does their neural network predict a single offset in a recording?

SOLUTION:
Yes