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: We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences.  
 Question: How do they perform data augmentation?

Output: They randomly sample sentences from Wikipedia that contains an object RC and add them to training data


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: Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task. 
 Question: On which task does do model do best?


Output: Variety prediction task