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.

[EX Q]: $Coherence@k$ has been shown to have high correlation with human interpretability of topics learned via various topic modeling methods BIBREF7 . Hence, we can expect interpretable embeddings by maximizing it. For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.
Question: How do they evaluate interpretability?
[EX A]: For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.

[EX Q]: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific.
Question: What does an individual model consist of?
[EX A]: Bayesian model of garg2012unsupervised as our base monolingual model

[EX Q]: Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds.
Question: What hypernymy tasks do they study?
[EX A]:
Detection Direction Graded Entailment