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 ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$). 
 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 ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$)

PROBLEM: Contexts are either ground-truth contexts from that dataset, or they are Wikipedia passages retrieved using TF-IDF BIBREF24 based on a HotpotQA question. A new non-overlapping set of contexts was again constructed from Wikipedia via HotpotQA using the same method as Round 1. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. 
 Question: What data sources do they use for creating their dataset?

SOLUTION: Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus) RTE5

PROBLEM: Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. 
 Question: How higher are F1 scores compared to previous work?

SOLUTION:
WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively

