  Text generation rarely considers the control of lexical complexity, which
limits its more comprehensive practical application. We introduce a novel task
of lexical complexity controlled sentence generation, which aims at keywords to
sentence generation with desired complexity levels. It has enormous potential
in domains such as grade reading, language teaching and acquisition. The
challenge of this task is to generate fluent sentences only using the words of
given complexity levels. We propose a simple but effective approach for this
task based on complexity embedding. Compared with potential solutions, our
approach fuses the representations of the word complexity levels into the model
to get better control of lexical complexity. And we demonstrate the feasibility
of the approach for both training models from scratch and fine-tuning the
pre-trained models. To facilitate the research, we develop two datasets in
English and Chinese respectively, on which extensive experiments are conducted.
Results show that our approach better controls lexical complexity and generates
higher quality sentences than baseline methods.
