Controllable Lexical Simplification for English

• URL: http://arxiv.org/abs/2302.02900v1
• Date: Mon, 6 Feb 2023 16:09:27 GMT
• Title: Controllable Lexical Simplification for English
• Authors: Kim Cheng Sheang, Daniel Ferr\'es, Horacio Saggion
• Abstract summary: We present a Controllable Lexical Simplification system fine-tuned with T5. Our model performs comparable to LSBert and even outperforms it in some cases.
• Score: 3.994126642748072
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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