A Bi-consolidating Model for Joint Relational Triple Extraction
<|reference_start|>A Bi-consolidating Model for Joint Relational Triple Extraction: Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence. In this paper, based on a two-dimensional sentence representation, a bi-consolidating model is proposed to address this problem by simultaneously reinforcing the local and global semantic features relevant to a relation triple. This model consists of a local consolidation component and a global consolidation component. The first component uses a pixel difference convolution to enhance semantic information of a possible triple representation from adjacent regions and mitigate noise in neighbouring neighbours. The second component strengthens the triple representation based a channel attention and a spatial attention, which has the advantage to learn remote semantic dependencies in a sentence. They are helpful to improve the performance of both entity identification and relation type classification in relation triple extraction. After evaluated on several publish datasets, the bi-consolidating model achieves competitive performance. Analytical experiments demonstrate the effectiveness of our model for relational triple extraction and give motivation for other natural language processing tasks.<|reference_end|>
@article{luo2024a,
    title={A Bi-consolidating Model for Joint Relational Triple Extraction},
    author={Xiaocheng Luo, Yanping Chen, Ruixue Tang, Caiwei Yang, Ruizhang Huang,
  Yongbin Qin},
    journal={arXiv preprint arXiv:2404.03881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03881},
    primaryClass={cs.CL}
}