BiSECT: Learning to Split and Rephrase Sentences with Bitexts
This addresses sentence simplification for NLP applications, with incremental improvements in dataset quality and model performance.
The paper tackles the NLP task of splitting complex sentences into shorter ones while rephrasing, by introducing BiSECT, a dataset of 1 million English sentence pairs and a model that targets specific regions for splitting and editing. The model trained on BiSECT improves upon previous state-of-the-art approaches in evaluations.
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.