Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

TL;DR

This paper introduces a novel graph-based sentence ordering framework that combines pairwise and set-to-sequence models through iterative prediction and achieves state-of-the-art results on multiple datasets.

Contribution

It proposes a new iterative framework that integrates pairwise classifiers with a graph-based model for improved sentence ordering.

Findings

Effective in predicting sentence orderings across datasets.

Achieves state-of-the-art performance with BERT and FHDecoder.

Demonstrates the benefit of combining pairwise and set-to-sequence models.

Abstract

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. 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 high-confident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model 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 and FHDecoder, our model achieves state-of-the-art performance.