An important task in NLP applications such as sentence simplification is the
ability to take a long, complex sentence and split it into shorter sentences,
rephrasing as necessary. We introduce a novel dataset and a new model for this
`split and rephrase' task. Our BiSECT training data consists of 1 million long
English sentences paired with shorter, meaning-equivalent English sentences. We
obtain these by extracting 1-2 sentence alignments in bilingual parallel
corpora and then using machine translation to convert both sides of the corpus
into the same language. BiSECT contains higher quality training examples than
previous Split and Rephrase corpora, with sentence splits that require more
significant modifications. We categorize examples in our corpus, and use these
categories in a novel model that allows us to target specific regions of the
input sentence to be split and edited. Moreover, we show that models trained on
BiSECT can perform a wider variety of split operations and improve upon
previous state-of-the-art approaches in automatic and human evaluations.

介绍了一个新的训练集 BiSECT 和针对 split and rephrase 任务的新模型。利用机器翻译从双语平行语料中提取长英语句子并与相应的较短、意思等效的英语句子配对。该模型针对输入句子中的特定区域进行分割和编辑，并利用训练集 BiSECT 在自动和人为评估中提高了先前 Split 和 Rephrase 模型的性能。