  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
https://github.com/ Sanqiang/text_simplification.
