LSBert: A Simple Framework for Lexical Simplification
TABLE III: Comparison of text simplification methods on WikiLarge dataset.
['[EMPTY]', 'Methods', 'SARI', 'FRES']
Our model LSBert obtains a SARI score of 39.37 and a FRES score of 77.07, even outperforming these three supervised TS systems (DRESS-LS, EditNTS and PBMT), which indicates that the model has indeed learned to simplify the complex sentences. Compared with LS methods Glavaš and REC-LS, LSBert achieves the best results. The two methods go to two different extremes, in which Glavaš simplifies almost all content words of one sentence and REC-LS prefers to save the original word. On the FRES metric, we can see that Glavaš outperforms LSBert, which is also because it simplifies almost all content words without caring for the equivalent meaning with the original sentence. Compared with Access, our model is highly competitive, because LSBert does not need a parallel dataset to learn and only focuses on simplifying the words. In conclusion, we can see that LSBert outperforms previous LS baselines, even some supervised TS baselines, which indicate that our method is effective at creating simpler output.