  With the advances of deep learning techniques, text generation is attracting
increasing interest in the artificial intelligence (AI) community, because of
its wide applications and because it is an essential component of AI.
Traditional text generation systems are trained in a supervised way, requiring
massive labeled parallel corpora. In this paper, I will introduce our recent
work on search and learning approaches to unsupervised text generation, where a
heuristic objective function estimates the quality of a candidate sentence, and
discrete search algorithms generate a sentence by maximizing the search
objective. A machine learning model further learns from the search results to
smooth out noise and improve efficiency. Our approach is important to the
industry for building minimal viable products for a new task; it also has high
social impacts for saving human annotation labor and for processing
low-resource languages.
