Controllable Text Simplification Using Lexically Constrained Decoding Based on Edit Operation Prediction

Abstract

In this study, we propose a method for controllable text simplification using lexically constrained decoding. Currently existing methods often leave out the difficult words in the output sentences and need more flexibility in sentence generation. The proposed method creates constraints for identifying words that should not appear in simple output sentences and those that should appear in output sentences. Three elements are involved in creating the constraints: edit operation prediction for each word in the sentence, difficulty determination based on a word-level lexicon, and replacement word identification. Then, a seq2seq (sequence-to-sequence) model based on lexical constraints simplifies text while controlling the difficulty level of the output sentence. The proposed method can simplify text according to the target difficulty level without losing grammatical correctness or disturbing the meaning of sentences.