Controllable Text Simplification with Explicit Paraphrasing
Mounica Maddela, Fernando Emilio Alva-Manchego, Wei Hong Xu · 2021
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.We introduce a new data augmentation method to improve the paraphrasing capability of our model.Through automatic and manual evaluations, we show that our proposed model establishes a new state-ofthe art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts. 1