论文标题
关于学习和改善无监督措施的搜索目标的实证研究
An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing
论文作者
论文摘要
多年来，无监督文本的研究一直在引起关注。最近的一种方法是针对启发式定义目标的本地搜索，该目标指定语言流利度，语义含义和其他特定于任务的属性。句子空间中的搜索是通过单词级编辑操作来实现的，包括插入，替换和删除。但是，这种目标函数是用多个组件手动设计的。尽管以前的工作表明，最大限度地提高了这一目标在成功度量方面取得了良好的表现（即Bleu和ibleu），但客观景观被认为是不平淡无奇的，具有巨大的声音，带来了优化的挑战。在本文中，我们通过学习建模搜索动态来解决启发式搜索目标中噪声的研究问题。然后，学习的模型与原始目标函数结合使用，以引导方式指导搜索。实验结果表明，与原始搜索目标相结合的学习模型确实可以提供平滑效果，从而提高了搜索性能。
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.
