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.

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

[Q]: In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied.
Question: Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
[A]: Yes

[Q]: Modern Standard Arabic (MSA) and Classical Arabic (CA) have two types of vowels, namely long vowels, which are explicitly written, and short vowels, aka diacritics, which are typically omitted in writing but are reintroduced by readers to properly pronounce words. We show that our model achieves a case ending error rate (CEER) of 3.7% for MSA and 2.5% for CA. For MSA, this CEER is more than 60% lower than other state-of-the-art systems such as Farasa and the RDI diacritizer, which are trained on the same dataset and achieve CEERs of 10.7% and 14.4% respectively.
Question: what are the previous state of the art?
[A]:
Farasa RDI