  Lexical simplification has attracted much attention in many languages, which
is the process of replacing complex words in a given sentence with simpler
alternatives of equivalent meaning. Although the richness of vocabulary in
Chinese makes the text very difficult to read for children and non-native
speakers, there is no research work for Chinese lexical simplification (CLS)
task. To circumvent difficulties in acquiring annotations, we manually create
the first benchmark dataset for CLS, which can be used for evaluating the
lexical simplification systems automatically. In order to acquire more thorough
comparison, we present five different types of methods as baselines to generate
substitute candidates for the complex word that include synonym-based approach,
word embedding-based approach, pretrained language model-based approach,
sememe-based approach, and a hybrid approach. Finally, we design the
experimental evaluation of these baselines and discuss their advantages and
disadvantages. To our best knowledge, this is the first study for CLS task.
