@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://preview.aclanthology.org/jlcl-multiple-ingestion/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}"
}