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.

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

Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary. 
 Question: What different correlations result when using different variants of ROUGE scores?
we observe that many variants of Rouge scores do not have high correlations with human pyramid scores

Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories. 
 Question: What are the weaknesses of their proposed interpretability quantification method?

can be biased by dataset used and may generate categories which are suboptimal compared to human designed categories