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。

一项关于学习和改进无监督释义搜索目标的实证研究

An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing

In this work, we present TGLS, a novel framework to unsupervised Text
Generation by Learning from Search. We start by applying a strong search
algorithm (in particular, simulated annealing) towards a heuristically defined
objective that (roughly) estimates the quality of sentences. Then, a
conditional generative model learns from the search results, and meanwhile
smooth out the noise of search. The alternation between search and learning can
be repeated for performance bootstrapping. We demonstrate the effectiveness of
TGLS on two real-world natural language generation tasks, paraphrase generation
and text formalization. Our model significantly outperforms unsupervised
baseline methods in both tasks. Especially, it achieves comparable performance
with the state-of-the-art supervised methods in paraphrase generation.

本研究提出了一种名为 TGLS 的无监督文本生成方法，采用模拟退火算法和条件生成模型相结合，取得了在同类方法中最佳的生成效果。

基于搜索学习的自监督文本生成

Unsupervised Text Generation by Learning from Search

Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore
supervised approaches to both graph-to-text generation and text-to-graph
knowledge extraction (semantic parsing) will always suffer from a shortage of
domain-specific parallel graph-text data; at the same time, adapting a model
trained on a different domain is often impossible due to little or no overlap
in entities and relations. This situation calls for an approach that (1) does
not need large amounts of annotated data and thus (2) does not need to rely on
domain adaptation techniques to work well in different domains. To this end, we
present the first approach to unsupervised text generation from KGs and show
simultaneously how it can be used for unsupervised semantic parsing. We
evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene
graphs from Visual Genome. Our system outperforms strong baselines for both
text$\leftrightarrow$graph conversion tasks without any manual adaptation from
one dataset to the other. In additional experiments, we investigate the impact
of using different unsupervised objectives.

通过无监督文本生成的方法，本研究旨在解决知识图谱不同领域自动转换的问题，同时，为了避免短缺的领域特定数据对自动转换造成的影响，本研究还提出了一种无监督语义分析的方法。