@inproceedings{lai-etal-2021-improving,
    title = "Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings",
    author = "Lai, Shaopeng  and
      Wang, Ante  and
      Meng, Fandong  and
      Zhou, Jie  and
      Ge, Yubin  and
      Zeng, Jiali  and
      Yao, Junfeng  and
      Huang, Degen  and
      Su, Jinsong",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.emnlp-main.186/",
    doi = "10.18653/v1/2021.emnlp-main.186",
    pages = "2407--2417",
    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 (Yin et al. 2019, 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 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 (Yin et al. 2019, 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 \url{https://github.com/DeepLearnXMU/IRSEG}."
}