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arXiv · 1611.04953

End-to-End Neural Sentence Ordering Using Pointer Network

Jingjing Gong·Xinchi Chen·Xipeng Qiu·Xuanjing Huang
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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.

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Jingjing Gong, Xinchi Chen, Xipeng Qiu, Xuanjing Huang. 2016-11-15. End-to-End Neural Sentence Ordering Using Pointer Network. https://arxiv.org/abs/1611.04953

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