  Sentence simplification reduces semantic complexity to benefit people with
language impairments. Previous simplification studies on the sentence level and
word level have achieved promising results but also meet great challenges. For
sentence-level studies, sentences after simplification are fluent but sometimes
are not really simplified. For word-level studies, words are simplified but
also have potential grammar errors due to different usages of words before and
after simplification. In this paper, we propose a two-step simplification
framework by combining both the word-level and the sentence-level
simplifications, making use of their corresponding advantages. Based on the
two-step framework, we implement a novel constrained neural generation model to
simplify sentences given simplified words. The final results on Wikipedia and
Simple Wikipedia aligned datasets indicate that our method yields better
performance than various baselines.
