论文标题
自动文本简化的嘈杂频道
Noisy Channel for Automatic Text Simplification
论文作者
论文摘要
在本文中，我们提出了一种基于嘈杂的通道方案简化自动句子的简单重新排列方法。根据语言模型，重新排列的方法不是直接计算出复杂文本的最佳简化，而是重新排列的方法还考虑了简单句子的概率以及简单文本本身的概率。我们的实验表明，将这些分数组合在三个不同的英语数据集中优于原始系统，从而在其中一个数据集中获得了最著名的结果。采用嘈杂的渠道方案为将其他信息注入ATS系统开辟了新的方法，从而控制了它们的重要方面，这是端到端神经SEQ2SEQ生成模型的已知局限性。
In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme. Instead of directly computing the best simplification given a complex text, the re-ranking method also considers the probability of the simple sentence to produce the complex counterpart, as well as the probability of the simple text itself, according to a language model. Our experiments show that combining these scores outperform the original system in three different English datasets, yielding the best known result in one of them. Adopting the noisy channel scheme opens new ways to infuse additional information into ATS systems, and thus to control important aspects of them, a known limitation of end-to-end neural seq2seq generative models.
