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.

Example Input:  Plackett-Luce Model for SMT Reranking
After being de-duplicated, the N-best list has an average size of around 300, and with 7491 features. This experiment displays, in large-scale features, the Plackett-Luce model correlates with BLEU score very well, and alleviates overfitting in some degree.
Example Output: What experiments with large-scale features are performed?

Example Input: In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments. The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates.
Example Output: Is datasets for sentiment analysis balanced?

Example Input: Concerning logical inferences, monotonicity reasoning BIBREF6 , BIBREF7 , which is a type of reasoning based on word replacement, requires the ability to capture the interaction between lexical and syntactic structures.
Example Output:
What is monotonicity reasoning?