Sentence simplification aims to reduce the complexity of a sentence while
retaining its original meaning. Current models for sentence simplification
adopted ideas from ma- chine translation studies and implicitly learned
simplification mapping rules from normal- simple sentence pairs. In this paper,
we explore a novel model based on a multi-layer and multi-head attention
architecture and we pro- pose two innovative approaches to integrate the Simple
PPDB (A Paraphrase Database for Simplification), an external paraphrase
knowledge base for simplification that covers a wide range of real-world
simplification rules. The experiments show that the integration provides two
major benefits: (1) the integrated model outperforms multiple state- of-the-art
baseline models for sentence simplification in the literature (2) through
analysis of the rule utilization, the model seeks to select more accurate
simplification rules. The code and models used in the paper are available at
this https URL Sanqiang/text_simplification.

本研究提出了一种基于多层和多头注意力架构的新颖模型，并提出了两种集成 Simple PPDB 的创新方法，实现对句子的简化。该研究表明，集成 Simple PPDB 的模型在句子简化中的表现优于现有基准模型，并能够准确定位并选择更精确的简化规则。