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        <title>A Multi-Gate Encoder for Joint Entity and Relation Extraction</title>
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    <abstract>“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.”</abstract>
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        <url>https://aclanthology.org/2022.ccl-1.75</url>
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%0 Conference Proceedings
%T A Multi-Gate Encoder for Joint Entity and Relation Extraction
%A Xiong, Xiong
%A Yunfei, Liu
%A Anqi, Liu
%A Shuai, Gong
%A Shengyang, Li
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G English
%F xiong-etal-2022-multi
%X “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.”
%U https://aclanthology.org/2022.ccl-1.75
%P 848-860

Markdown (Informal)

[A Multi-Gate Encoder for Joint Entity and Relation Extraction](https://aclanthology.org/2022.ccl-1.75) (Xiong et al., CCL 2022)

• A Multi-Gate Encoder for Joint Entity and Relation Extraction (Xiong et al., CCL 2022)

ACL

• Xiong Xiong, Liu Yunfei, Liu Anqi, Gong Shuai, and Li Shengyang. 2022. A Multi-Gate Encoder for Joint Entity and Relation Extraction. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 848–860, Nanchang, China. Chinese Information Processing Society of China.