Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning

• URL: http://arxiv.org/abs/2106.01559v1
• Date: Thu, 3 Jun 2021 02:57:08 GMT
• Title: Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning
• Authors: Fubang Zhao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu
• Abstract summary: We show that all of the fact extraction models can be organized according to a graph-oriented analytical perspective. An efficient model, aDjacency lIst oRientational faCT (Direct), is proposed based on this analytical framework.
• Score: 24.77542721790553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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