Sentence Ordering by Context-Enhanced Pairwise Comparison

Haowei Du, Jizhi Tang, Dongyan Zhao

Abstract: Sentence ordering is a task arranging the given unordered text into the correct order. A feasible approach is to use neural networks to predict the relative order of all sentence pairs and then organize the sentences into a coherent paragraph with topological sort. However, current methods rarely utilize the context information, which is essential for deciding the relative order of the sentence pair. Based on this observation, we propose an efficient approach context-enhanced pairwise comparison network (CPCN) that leverages both the context and sentence pair information in a post-fusion manner to order a sentence pair. To obtain the paragraph context embedding, CPCN first utilizes BERT to encode all sentences, then aggregates them using a Transformer followed by an average pooling layer. Finally, CPCN predicts the relative order of the sentence pair by the concatenation of the paragraph embedding and the sentence pair embedding. Our experiments on three benchmark datasets, SIND, NIPS and AAN show that our model outperforms all the existing models significantly and achieves a new state-of-the-art performance, which demonstrates the effectiveness of incorporating context information.