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.

[Q]: Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term "fast" pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term "fast" refers to.
[A]: How does car speak pertains to a car's physical attributes?

[Q]: The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs.
[A]: How well did the baseline perform?

[Q]: We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency.
[A]:
What previously proposed rules for predicting binoial ordering are used?