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: We conducted an experiment to determine whether we could maintain or improve classifier performances by applying the following three-tiered feature elimination approach:

Reduction We reduced the dataset encoded for each class by eliminating features that occur less than twice in the full dataset.

Selection We iteratively applied Chi-Square feature selection on the reduced dataset, selecting the top percentile of highest ranked features in increments of 5 percent to train and test the support vector model using a linear kernel and 5-fold, stratified cross-validation.

Rank We cumulatively plotted the average F1-score performances of each incrementally added percentile of top ranked features. We report the percentile and count of features resulting in the first occurrence of the highest average F1-score for each class.
Example Output: What are the three steps to feature elimination?

Example Input: FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet.
Example Output: What is the WordNet counterpart for Persian?

Example Input: It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy.
Example Output:
What are least important components identified in the the training of VQA models?

