@inproceedings{xiong-etal-2022-multi,
title = "A Multi-Gate Encoder for Joint Entity and Relation Extraction",
author = "Xiong, Xiong and
Yunfei, Liu and
Anqi, Liu and
Shuai, Gong and
Shengyang, Li",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.75/",
pages = "848--860",
language = "eng",
abstract = "{\textquotedblleft}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.{\textquotedblright}"
}
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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
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%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 eng
%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)
ACL