Papers › A Multi-Gate Encoder for Joint Entity and Relation Extraction

A Multi-Gate Encoder for Joint Entity and Relation Extraction

1 Oct 2022CCL 2022 10archive 2025-07-28

Xiong Xiong, Liu Yunfei, Liu Anqi, Gong Shuai, Li Shengyang

“Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge difficulty. We propose a multi-gate encoder that models bidirectional task interaction while keeping sufficient feature specificity based on gating mechanism in this paper. Precisely, we design two types of independent gates: task gates to generate task-specific features and interaction gates to generate instructive features to guide the opposite task. Our experiments show that our method increases the state-of-the-art (SOTA) relation F1 scores on ACE04, ACE05 and SciERC datasets to 63.8% (+1.3%), 68.2% (+1.4%), 39.4% (+1.0%), respectively, with higher inference speed over previous SOTA model.”

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