The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine's understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional text-to-marker generation problem. We present Reorder-BART (Re-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text. Re-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall's tau ($\tau$). We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform several experiments to understand the functioning and limitations of our framework.

翻译：将一组打乱的句子编成一套连贯的文本的任务被用来评价机器对因果关系和时间关系的理解。我们把命令任务作为有条件的文本到标记生成问题来拟订。我们提出重订-BART(Re-BART),利用一个预先训练的变压器模型来为一套打乱的句子确定一致的顺序。模型采用一套打乱的句子,将特定句子标记作为输入,并产生一个定购文本中句子的位置标记序列。重新BART在完美匹配比率(PMR)和Kendall's Tau($\tau$)的7个数据集中实现了最先进的性能。我们用零发式来进行评估,显示我们的模型能够将其他数据集综合起来。我们还进行了一些实验,以了解我们框架的功能和局限性。