Recently, a large pre-trained language model
called T5 (A Unified Text-to-Text Transfer
Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no
study has been found using this pre-trained
model on Text Simplification. Therefore in
this paper, we explore the use of T5 fine-tuning
on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show
that ...
Recently, a large pre-trained language model
called T5 (A Unified Text-to-Text Transfer
Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no
study has been found using this pre-trained
model on Text Simplification. Therefore in
this paper, we explore the use of T5 fine-tuning
on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show
that our model achieves remarkable results
with gains of between +0.69 and +1.41 over
the current state-of-the-art (BART+ACCESS).
We argue that using a pre-trained model such
as T5, trained on several tasks with large
amounts of data, can help improve Text Simplification.
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