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]: The neural machine translation was trained using Nematus. For the NMT system as well as for the PreMT system, we used the default configuration.
[A]: Which NMT architecture do they use?

[Q]: Each training sample is generated by three steps: align, mask and select, which we call as AMS method. Each sample in the dataset consists of a question and several candidate answers, which has the same form as the CommonsenseQA dataset.
[A]: How do they select answer candidates for their QA task?

[Q]: To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0.
[A]:
Do they evaluate on NER data sets?