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Controllable Sentence Simplification Using Transfer Learning

The CLEF 2022 SimpleText track addresses the challenges of text simplification approaches to generate versions of scientific texts for a non-expert audience in highly technical domains, such as Computer Science or Medicine. Our work uses the transfer learning capabilities of the T5 pre-trained language model, adding a method to control specific simplification features. We present a new feature based on masked tokens prediction (Language Model Fill-Mask) to control the lexical complexity of the text generation process. The results obtained with the SARI metric are at the same level as previous work in other domains for sentence simplification.