  Solving text classification in a weakly supervised manner is important for
real-world applications where human annotations are scarce. In this paper, we
propose to query a masked language model with cloze style prompts to obtain
supervision signals. We design a prompt which combines the document itself and
"this article is talking about [MASK]." A masked language model can generate
words for the [MASK] token. The generated words which summarize the content of
a document can be utilized as supervision signals. We propose a latent variable
model to learn a word distribution learner which associates generated words to
pre-defined categories and a document classifier simultaneously without using
any annotated data. Evaluation on three datasets, AGNews, 20Newsgroups, and
UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.
