  Sentence ordering aims to arrange the sentences of a given text in the
correct order. Recent work frames it as a ranking problem and applies deep
neural networks to it. In this work, we propose a new method, named BERT4SO, by
fine-tuning BERT for sentence ordering. We concatenate all sentences and
compute their representations by using multiple special tokens and carefully
designed segment (interval) embeddings. The tokens across multiple sentences
can attend to each other which greatly enhances their interactions. We also
propose a margin-based listwise ranking loss based on ListMLE to facilitate the
optimization process. Experimental results on five benchmark datasets
demonstrate the effectiveness of our proposed method.
