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.

Ex Input:
We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions.
Question: what is the size of BoolQ dataset?

Ex Output:
16k questions

Ex Input:
Three datasets annotated with emotions are commonly used for the development and evaluation of emotion detection systems, namely the Affective Text dataset, the Fairy Tales dataset, and the ISEAR dataset. A summary is provided in Table TABREF8 , which also shows, in the bottom row, what role each dataset has in our experiments: apart from the development portion of the Affective Text, which we used to develop our models (Section SECREF4 ), all three have been used as benchmarks for our evaluation.
Question: Which existing benchmarks did they compare to?

Ex Output:
Affective Text Fairy Tales ISEAR

Ex Input:
We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.
Question: How does different parameter settings impact the performance and semantic capacity of resulting model?

Ex Output:
number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others