  The task of Split and Rephrase, which splits a complex sentence into multiple
simple sentences with the same meaning, improves readability and enhances the
performance of downstream tasks in natural language processing (NLP). However,
while Split and Rephrase can be improved using a text-to-text generation
approach that applies encoder-decoder models fine-tuned with a large-scale
dataset, it still suffers from hallucinations and under-splitting. To address
these issues, this paper presents a simple and strong data refinement approach.
Here, we create WikiSplit++ by removing instances in WikiSplit where complex
sentences do not entail at least one of the simpler sentences and reversing the
order of reference simple sentences. Experimental results show that training
with WikiSplit++ leads to better performance than training with WikiSplit, even
with fewer training instances. In particular, our approach yields significant
gains in the number of splits and the entailment ratio, a proxy for measuring
hallucinations.
