StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

Xuetao Tian, Liping Jing, Lu He, Feng Liu


Abstract
Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.
Anthology ID:
2021.acl-long.375
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4851–4861
Language:
URL:
https://aclanthology.org/2021.acl-long.375
DOI:
10.18653/v1/2021.acl-long.375
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.375.pdf