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.

Example Input: $Coherence@k$ 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?
Example Output: For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.

Example Input: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space.
Question: How do the authors define a differential network?
Example Output: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.

Example Input: To validate the effectiveness of AEM for extracting events from social media (e.g. Twitter) and news media sites (e.g. Google news), three datasets (FSD BIBREF12 , Twitter, and Google datasets) are employed.
Question: What datasets are used?
Example Output:
FSD BIBREF12 , Twitter, and Google datasets