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.
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Question: We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network. 
 Question: How do they measure correlation between the prediction and explanation quality?

Answer: They look at the performance accuracy of explanation and the prediction performance


Question: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?

Answer: weighted factorization of a word-context co-occurrence matrix 


Question: To tackle this issue we applying a back-translation method BIBREF13, where we translate indirect and physical harassment tweets of the train set from english to german, french and greek. After that, we translate them back to english in order to achieve data augmentation. 
 Question: What language(s) is/are represented in the dataset?

Answer:
english