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.

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

In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing.
Question: Which of their training domains improves performance the most?
documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

The ensembles were formed by simply averaging the predictions from the constituent single models.
Question: How does their ensemble method work?
simply averaging the predictions from the constituent single models