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.

[EX Q]: In particular, we investigated attention to nouns, verbs, pronouns, subjects, objects, and negation words, and special BERT tokens across the tasks.
[EX A]: What handcrafter features-of-interest are used?

[EX Q]: The multi-task model outperforms the single-task model at all data sizes, and the relative performance increases as the size of the training data decreases. When only 200 sentences of training data are used, the performance of the multi-task model is about 60% better than the single-task model for both the Airbnb and Greyhound apps. The relative gain for the OpenTable app is 26%. The results differ between the tasks, but none have an overall benefit from the open vocabulary system.
[EX A]: Does the performance increase using their method?

[EX Q]: RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. The RKS method provides an approximate kernel function via explicit mapping.
[EX A]:
What is the Random Kitchen Sink approach?