Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification
Tatsuya Zetsu, Tomoyuki Kajiwara, Yuki Arase · 2022
Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level.However, existing methods tend to perform conservative edits that keep complex words intact.To address this problem, we employ lexically constrained decoding to encourage rewriting.Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence.The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.