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: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets.
Question: What metrics are used for evaluation?
Example Output: ROUGE BIBREF29 and METEOR BIBREF30

Example Input: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?
Example Output: 27.41 transformation on average of single seed sentence is available in dataset.

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.