Controllable Text Simplification with Explicit Paraphrasing

Abstract

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-of-the-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.

References in corpus (7)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• BERTScore: Evaluating Text Generation with BERT
• Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
• Neural Programmer-Interpreters
• Neural Cross-Lingual Entity Linking
• Neural Networks Models for Entity Discovery and Linking
• Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints