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 Input:
We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., "If a surface has more friction, then an object will travel slower"), and asked to enter two objects, people, or situations to compare.

Ex Output:
Do all questions in the dataset allow the answers to pick from 2 options?

Ex Input:
Our main contributions are (1) the publication of Turkish corpus for coarse-grained and fine-grained NER, and TC research, (2) six different versions of corpus according to noise reduction methodology and entity types, (3) an analysis of the corpus and (4) benchmark comparisons for NER and TC tasks against human annotators.

Ex Output:
Did they experiment with the dataset on some tasks?

Ex Input:
This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously.

Ex Output:
What type of classifiers are used?