Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

• URL: http://arxiv.org/abs/2110.06446v1
• Date: Wed, 13 Oct 2021 02:18:16 GMT
• Title: Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
• Authors: Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang and Jinsong Su
• Abstract summary: We propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. Our model achieves state-of-the-art performance when equipped with BERT and FHDecoder.
• Score: 38.91604447717656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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