Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning
Fubang Zhao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu · 2021
Relational fact extraction aims to extract semantic triplets from unstructured text.In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective.An efficient model, aDjacency lIst oRiented rElational faCT (DIRECT), is proposed based on this analytical framework.To alleviate challenges of error propagation and sub-task loss equilibrium, DIRECT employs a novel adaptive multi-task learning strategy with dynamic sub-task loss balancing.Extensive experiments are conducted on two benchmark datasets, and results prove that the proposed model outperforms a series of state-of-the-art (SoTA) models for relational triplet extraction.