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:  Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).  
 Question: Which machine learning models are used?

Answer: RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel


Question: 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?

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


Question: Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). 
 Question: What metrics are used in evaluation of this task?

Answer:
F1 score