Controllable Text Simplification with Explicit Paraphrasing

Understand

Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting.

• Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously.
• However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences.
• In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles.