End-to-End Neural Sentence Ordering Using Pointer Network
Computation and Language
2016-11-28 v2
Abstract
Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes and dataset of this paper are available.
Cite
@article{arxiv.1611.04953,
title = {End-to-End Neural Sentence Ordering Using Pointer Network},
author = {Jingjing Gong and Xinchi Chen and Xipeng Qiu and Xuanjing Huang},
journal= {arXiv preprint arXiv:1611.04953},
year = {2016}
}