A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification

Abstract

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.

References in corpus (5)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• ADADELTA: An Adaptive Learning Rate Method
• Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
• Backward and Forward Language Modeling for Constrained Sentence Generation

Cited by in corpus (5)

• Evaluating Transfer Learning for Simplifying GitHub READMEs
• Open Information Extraction from Question-Answer Pairs
• EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
• BLEU is Not Suitable for the Evaluation of Text Simplification
• Simple and Effective Text Simplification Using Semantic and Neural Methods