In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details).
[A]: How do slot binary classifiers improve performance?

[Q]: Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naive Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$. A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule; A Naive Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
[A]: How does the parameter-free model work?

[Q]: $D_a$ contained all of the tweets collected on the attack day of the five attacks mentioned in section 4.2. And $D_b$ contained all of the tweets collected before the five attacks. There are 1180 tweets in $D_a$ and 7979 tweets in $D_b$. The tweets on the attack days ($D_a$) are manually annotated and only 50 percent of those tweets are actually about a DDoS attack.
[A]:
Do twitter users tend to tweet about the DOS attack when it occurs? How much data supports this assumption?