What is it about?
In this paper, we propose a simple but effective bidirectional framework for relational triple extraction. It can well address the "ground entity extraction failure issue" which is widely existed in most SOTA tagging based methods. Besides, we observe the convergence rate inconsistency issue existed in the share structures, and propose a share-aware learning mechanism to address it. Our experiments show that the proposed method is very effective.
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Why is it important?
The proposed bidirectional framework is simple, effective and adaptive, which makes it perform well when deployed in real scenarios.
Perspectives
This paper proposes a simple but effective method that can perform well when deployed in real scenarios.
Feiliang Ren
Northeastern University
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This page is a summary of: A Simple but Effective Bidirectional Framework for Relational Triple Extraction, February 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3488560.3498409.
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