@inproceedings{li-etal-2023-janko,
    title = "Janko at {S}em{E}val-2023 Task 2: Bidirectional {LSTM} Model Based on Pre-training for {C}hinese Named Entity Recognition",
    author = "Li, Jiankuo  and
      Guan, Zhengyi  and
      Ding, Haiyan",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.semeval-1.132/",
    doi = "10.18653/v1/2023.semeval-1.132",
    pages = "958--962",
    abstract = "This paper describes the method we submitted as the Janko team in the SemEval-2023 Task 2,Multilingual Complex Named Entity Recognition (MultiCoNER 2). We only participated in the Chinese track. In this paper, we implement the BERT-BiLSTM-RDrop model. We use the fine-tuned BERT models, take the output of BERT as the input of the BiLSTM network, and finally use R-Drop technology to optimize the loss function. Our submission achieved a macro-averaged F1 score of 0.579 on the testset."
}