@inproceedings{cao-ananiadou-2021-generativere-incorporating,
    title = "{G}enerative{RE}: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction",
    author = "Cao, Jiarun  and
      Ananiadou, Sophia",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.findings-emnlp.182/",
    doi = "10.18653/v1/2021.findings-emnlp.182",
    pages = "2119--2126",
    abstract = "Previous neural Seq2Seq models have shown the effectiveness for jointly extracting relation triplets. However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. To tackle these problems, we propose a generative, multi-task learning framework, named GenerativeRE. We firstly propose a special entity labelling method on both input and output sequences. During the training stage, GenerativeRE fine-tunes the pre-trained generative model and learns the special entity labels simultaneously. During the inference stage, we propose a novel copy mechanism equipped with three mask strategies, to generate the most probable tokens by diminishing the scope of the model decoder. Experimental results show that our model achieves 4.6{\%} and 0.9{\%} F1 score improvements over the current state-of-the-art methods in the NYT24 and NYT29 benchmark datasets respectively."
}