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]: For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter.
[EX A]: What dataset is used for this study?

[EX Q]: We conduct our experiments on $\sim $ 8.7M annotated anonymised user utterances. They are annotated and derived from requests across 23 domains.
[EX A]: Over which datasets/corpora is this work evaluated?

[EX Q]: In this work, we introduce a new logical inference engine called MonaLog, which is based on natural logic and work on monotonicity stemming from vanBenthemEssays86. Since our logic operates over surface forms, it is straightforward to hybridize our models. We investigate using MonaLog in combination with the language model BERT BIBREF20, including for compositional data augmentation, i.e, re-generating entailed versions of examples in our training sets.  We perform two experiments to test MonaLog. We first use MonaLog to solve the problems in a commonly used natural language inference dataset, SICK BIBREF1, comparing our results with previous systems. Second, we test the quality of the data generated by MonaLog. To do this, we generate more training data (sentence pairs) from the SICK training data using our system, and performe fine-tuning on BERT BIBREF20, a language model based on the transformer architecture BIBREF23, with the expanded dataset. 
[EX A]:
How do they combine MonaLog with BERT?