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]: We evaluate our approaches on two public Chinese span-extraction machine reading comprehension datasets: CMRC 2018 (simplified Chinese) BIBREF8 and DRCD (traditional Chinese) BIBREF9. The statistics of the two datasets are listed in Table TABREF29.
[A]: Is this a span-based (extractive) QA task?

[Q]: The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes.
[A]: What type of models are used for classification?

[Q]: The semi-parametric models were trained on 32 GPUs with each replica split over 2 GPUs, one to train the translation model and the other for computing the CSTM.
[A]:
Does their combination of a non-parametric retrieval and neural network get trained end-to-end?