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: he immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}ig (q_\phi ({z}|{x}) || p({z})ig )-C|$).
Question: Why does proposed term help to avoid posterior collapse?

SOLUTION: by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}ig (q_\phi ({z}|{x}) || p({z})ig )-C|$)

PROBLEM: 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?

SOLUTION: documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

PROBLEM: We use two datasets, NELL-One and Wiki-One which are constructed by BIBREF11 .
Question: What datasets are used to evaluate the approach?

SOLUTION:
NELL-One, Wiki-One