  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 (LexMTurk, 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.
