Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
Q: 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.
Question: Is it a neural model? How is it trained?
A:
No, it is a probabilistic model trained by finding feature weights through gradient ascent