In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: We manually reviewed 1,177 pairs of entities and referring expressions generated by the system. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions.

Answer: How is fluency of generated text evaluated?


Question: GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias. In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied. We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other. Our methods are also evaluated on datasets with different unbalanced class distributions. We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents. Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive.

Answer: How do they define robustness of a model?


Question: We collected all available comments in the stories from Reddit from August 2015. 

Answer:
what is the source of the new dataset?