Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al., 2019 (Yin et al., , 2021)) . Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted highconfident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model (Yin et al., 2019 (Yin et al., , 2021) ) on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT (Devlin et al., 2019) and FHDecoder (Yin et al., 2020), our model achieves state-of-the-art performance. Our code is available at https:// github.com/DeepLearnXMU/IRSEG .