Controllable Text Simplification with Lexical Constraint

Abstract

We propose a method for controlling the sentence complexity in text simplification tasks. Our text simplification method is successful in translating an input sentence into a specific grade level by considering the complexities of both sentences and words. Sentence complexities are considered by adding the target grade level to an input. In contrast, a word complexity is considered by following three different methods: (a) by extending word embedding by adding a grade level as a feature, (b) by imposing a hard constraint that prohibits outputting complex words, and (c) by imposing a soft constraint that encourages outputting of simple words. Results from the experiments indicate that a soft constraint improves the performance of a text simplification task. Although the existing model that considers only the sentence complexity may control the syntactic complexity aspects like omission, it tends to generate words beyond the target complexity. Our method achieves the control of both syntactic structures and lexical complexities.