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.

Input: Consider Input: We evaluate performance of our models on the SQUAD BIBREF16 dataset (denoted $\mathcal {S}$ ). We use the same split as that of BIBREF4 , where a random subset of 70,484 instances from $\mathcal {S}\ $ are used for training ( ${\mathcal {S}}^{tr}$ ), 10,570 instances for validation ( ${\mathcal {S}}^{val}$ ), and 11,877 instances for testing ( ${\mathcal {S}}^{te}$ ).

Output: Which datasets are used to train this model?


Input: Consider Input: In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:

we fully connect nodes that represent sentences from the same passage (dotted-black);

we fully connect nodes that represent the first sentence of each passage (dotted-red);

we add an edge between the question and every node for each passage (dotted-blue).

Output: How are some nodes initially connected based on text structure?


Input: Consider Input: Here we describe the components of our probabilistic model of question generation.  The details of optimization are as follows. First, a large set of 150,000 questions is sampled in order to approximate the gradient at each step via importance sampling. Second, to run the procedure for a given model and training set, we ran 100,000 iterations of gradient ascent at a learning rate of 0.1.


Output: Is it a neural model? How is it trained?