  Lexical simplification (LS) aims to replace complex words in a given sentence
with their simpler alternatives of equivalent meaning, to simplify the
sentence. Recently unsupervised lexical simplification approaches only rely on
the complex word itself regardless of the given sentence to generate candidate
substitutions, which will inevitably produce a large number of spurious
candidates. In this paper, we propose a lexical simplification framework LSBert
based on pretrained representation model Bert, that is capable of (1) making
use of the wider context when both detecting the words in need of
simplification and generating substitue candidates, and (2) taking five
high-quality features into account for ranking candidates, including Bert
prediction order, Bert-based language model, and the paraphrase database PPDB,
in addition to the word frequency and word similarity commonly used in other LS
methods. We show that our system outputs lexical simplifications that are
grammatically correct and semantically appropriate, and obtains obvious
improvement compared with these baselines, outperforming the state-of-the-art
by 29.8 Accuracy points on three well-known benchmarks.

Computation and Language;Information Retrieval