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: Our system learns Perceptron models BIBREF37 using the Machine Learning machinery provided by the Apache OpenNLP project with our own customized (local and clustering) features.  The local features constitute our baseline system on top of which the clustering features are added.
Example Output: what are the baselines?

Example Input: Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.
Example Output: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?

Example Input: String kernels represent a way of using information at the character level by measuring the similarity of strings through character n-grams.
Example Output:
What is a string kernel?