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: Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks. 
 Question: What hyperparameters have been tuned?

SOLUTION: number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks

PROBLEM: For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. 
 Question: What evaluation metrics do they use?

SOLUTION: Precision, Recall and INLINEFORM0 score

PROBLEM: Outlier Detection. The Outlier Detection task BIBREF0 is to determine which word in a list INLINEFORM0 of INLINEFORM1 words is unrelated to the other INLINEFORM2 which were chosen to be related. For each INLINEFORM3 , one can compute its compactness score INLINEFORM4 , which is the compactness of INLINEFORM5 . Sentiment analysis. We also consider sentiment analysis as described by BIBREF31 . 
 Question: Do they test their word embeddings on downstream tasks?

SOLUTION:
Yes