  Research in unsupervised text generation has been gaining attention over the
years. One recent approach is local search towards a heuristically defined
objective, which specifies language fluency, semantic meanings, and other
task-specific attributes. Search in the sentence space is realized by
word-level edit operations including insertion, replacement, and deletion.
However, such objective function is manually designed with multiple components.
Although previous work has shown maximizing this objective yields good
performance in terms of true measure of success (i.e. BLEU and iBLEU), the
objective landscape is considered to be non-smooth with significant noises,
posing challenges for optimization. In this dissertation, we address the
research problem of smoothing the noise in the heuristic search objective by
learning to model the search dynamics. Then, the learned model is combined with
the original objective function to guide the search in a bootstrapping fashion.
Experimental results show that the learned models combined with the original
search objective can indeed provide a smoothing effect, improving the search
performance by a small margin.
