Is Everything in Order? A Simple Way to Order Sentences
Somnath Basu Roy Chowdhury, Faeze Brahman, Snigdha Chaturvedi · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 textto-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 (τ ).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.