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.

Input: Consider 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?

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


Input: Consider Input: In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?

Output: using word2vec to create features that are used as input to the SVM


Input: Consider Input: During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks. 
 Question: What are three main machine translation tasks?


Output: De-En, En-Fr and En-Vi translation tasks


