  Topic models have been successfully used for analyzing text documents.
However, with existing topic models, many documents are required for training.
In this paper, we propose a neural network-based few-shot learning method that
can learn a topic model from just a few documents. The neural networks in our
model take a small number of documents as inputs, and output topic model
priors. The proposed method trains the neural networks such that the expected
test likelihood is improved when topic model parameters are estimated by
maximizing the posterior probability using the priors based on the EM
algorithm. Since each step in the EM algorithm is differentiable, the proposed
method can backpropagate the loss through the EM algorithm to train the neural
networks. The expected test likelihood is maximized by a stochastic gradient
descent method using a set of multiple text corpora with an episodic training
framework. In our experiments, we demonstrate that the proposed method achieves
better perplexity than existing methods using three real-world text document
sets.
