Controllable Lexical Simplification for English
Kim Cheng Sheang, Daniel Ferrés, Horacio Saggion · 2022
Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task.However, so far, no research has applied similar approaches to the Lexical Simplification (LS) task.In this paper, we present ConLS, a Controllable Lexical Simplification system fine-tuned with T5 (a Transformer-based model pre-trained with a BERT-style approach and several other tasks).The evaluation results on three datasets (LexM-Turk, BenchLS, and NNSeval) have shown that our model performs comparable to LSBert (the current state-of-the-art) and even outperforms it in some cases.We also conducted a detailed comparison on the effectiveness of control tokens to give a clear view of how each token contributes to the model.