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]: To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI.
[EX A]: What NLI models do they analyze?

[EX 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.
[EX A]: How does car speak pertains to a car's physical attributes?

[EX Q]: The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model. 
[EX A]:
What do the models that they compare predict?