@inproceedings{skachkova-etal-2023-multilingual,
    title = "Multilingual coreference resolution: Adapt and Generate",
    author = "Skachkova, Natalia  and
      Anikina, Tatiana  and
      Mokhova, Anna",
    editor = "{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Ogrodniczuk, Maciej",
    booktitle = "Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.crac-sharedtask.2/",
    doi = "10.18653/v1/2023.crac-sharedtask.2",
    pages = "19--33",
    abstract = "The paper presents two multilingual coreference resolution systems submitted for the CRAC Shared Task 2023. The DFKI-Adapt system achieves 61.86 F1 score on the shared task test data, outperforming the official baseline by 4.9 F1 points. This system uses a combination of different features and training settings, including character embeddings, adapter modules, joint pre-training and loss-based re-training. We provide evaluation for each of the settings on 12 different datasets and compare the results. The other submission DFKI-MPrompt uses a novel approach that involves prompting for mention generation. Although the scores achieved by this model are lower compared to the baseline, the method shows a new way of approaching the coreference task and provides good results with just five epochs of training."
}