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: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific. 
 Question: What does an individual model consist of?

SOLUTION: Bayesian model of garg2012unsupervised as our base monolingual model

PROBLEM: To investigate the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods, we first study whether there are significant, strong and meaningful correlations between the terms present in each corpus and the many neighbourhood attributes through the Pearson correlation coefficient $\rho $ . 
 Question: What do the correlation demonstrate? 

SOLUTION: the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods

PROBLEM: For the 2019 Workshop on Neural Generation of Text (WNGT) Efficiency shared task BIBREF0, the Notre Dame Natural Language Processing (NDNLP) group looked at a method of inducing sparsity in parameters called auto-sizing in order to reduce the number of parameters in the Transformer at the cost of a relatively minimal drop in performance. 
 Question: What is WNGT 2019 shared task?

SOLUTION:
efficiency task aimed  at reducing the number of parameters while minimizing drop in performance