BiSECT：学习使用双文本拆分和重组句子
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
摘要：
NLP应用程序中的一个重要任务，如句子简化，是能够将一个长的复杂句子拆分成较短的句子，并在必要时重新措辞。我们引入了一个新的数据集和一个新的模型，这个'分裂和改写'的任务。我们的BiSECT训练数据由100万个英语长句和较短的、意义等同的英语句子组成。我们通过在双语平行语料库中提取1-2个句子对齐，然后使用机器翻译将语料库的两边转换为相同的语言来获得这些。BiSECT包含比以前的Split和Rephrase语料库更高质量的训练样本，句子分割需要更大的修改。我们对语料库中的示例进行分类，并在一个新的模型中使用这些类别，该模型允许我们针对输入句子的特定区域进行拆分和编辑。此外，我们表明，在BiSECT上训练的模型可以执行更广泛的分割操作，并在自动和人工评估中改进了以前最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
