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.

本文研究无监督文本生成技术中的优化问题，提出利用学习模型平滑启发式搜索目标函数中的噪声以及与原始目标函数相结合来指导搜索，实验表明，这种方法可以略微提高搜索性能。