Learning to Paraphrase Sentences to Different Complexity Levels
Alison Chi Li-Kuang Chen Yi-Chen Chang Shu-Hui Lee Jason S. Chang
While sentence simplification is an active research topic in NLP, its
adjacent tasks of sentence complexification and same-level paraphrasing are
not. To train models on all three tasks, we present two new unsupervised
datasets. We compare these datasets, one labeled by a weak classifier and the
other by a rule-based approach, with a single supervised dataset. Using these
three datasets for training, we perform extensive experiments on both
multitasking and prompting strategies. Compared to other systems trained on
unsupervised parallel data, models trained on our weak classifier labeled
dataset achieve state-of-the-art performance on the ASSET simplification
benchmark. Our models also outperform previous work on sentence level
targeting. Finally, we establish how a handful of Large Language Models perform
on these tasks under a zero-shot setting.