@inproceedings{han-etal-2024-conjoin,
    title = "Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition",
    author = "Han, Chengcheng  and
      Zhu, Renyu  and
      Kuang, Jun  and
      Chen, Fengjiao  and
      Li, Xiang  and
      Gao, Ming  and
      Cao, Xuezhi  and
      Xian, Yunsen",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.329",
    pages = "3707--3717",
    abstract = "Prompt-based methods have been widely used in few-shot named entity recognition (NER). In this paper, we first conduct a preliminary experiment and observe that the key to affecting the performance of prompt-based NER models is the capability to detect entity boundaries. However, most existing models fail to boost such capability. To solve the issue, we propose a novel model, ParaBART, which consists of a BART encoder and a specially designed parabiotic decoder. Specifically, the parabiotic decoder includes two BART decoders and a conjoint module. The two decoders are responsible for entity boundary detection and entity type classification, respectively. They are connected by the conjoint module, which is used to replace unimportant tokens{'} embeddings in one decoder with the average embedding of all the tokens in the other. We further present a novel boundary expansion strategy to enhance the model{'}s capability in entity type classification. Experimental results show that ParaBART can achieve significant performance gains over state-of-the-art competitors.",
}

Prompt-based methods have been widely used in few-shot named entity recognition (NER). In this paper, we first conduct a preliminary experiment and observe that the key to affecting the performance of prompt-based NER models is the capability to detect entity boundaries. However, most existing models fail to boost such capability. To solve the issue, we propose a novel model, ParaBART, which consists of a BART encoder and a specially designed parabiotic decoder. Specifically, the parabiotic decoder includes two BART decoders and a conjoint module. The two decoders are responsible for entity boundary detection and entity type classification, respectively. They are connected by the conjoint module, which is used to replace unimportant tokens{'} embeddings in one decoder with the average embedding of all the tokens in the other. We further present a novel boundary expansion strategy to enhance the model{'}s capability in entity type classification. Experimental results show that ParaBART can achieve significant performance gains over state-of-the-art competitors.
Han, Chengcheng Zhu, Renyu Kuang, Jun Chen, Fengjiao Li, Xiang Gao, Ming Cao, Xuezhi Xian, Yunsen
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition