@inproceedings{hofer-mottahedin-2023-minanto,
    title = "Minanto at {S}em{E}val-2023 Task 2: Fine-tuning {XLM}-{R}o{BERT}a for Named Entity Recognition on {E}nglish Data",
    author = {H{\"o}fer, Antonia  and
      Mottahedin, Mina},
    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-1.156/",
    doi = "10.18653/v1/2023.semeval-1.156",
    pages = "1127--1130",
    abstract = "Within the scope of the shared task MultiCoNER II our aim was to improve the recognition of named entities in English. We as team Minanto fine-tuned a cross-lingual model for Named Entity Recognition on English data and achieved an average F1 score of 51.47{\textbackslash}{\%} in the final submission. We found that a monolingual model works better on English data than a cross-lingual and that the input of external data from earlier Named Entity Recognition tasks provides only minor improvements. In this paper we present our system, discuss our results and analyze the impact of external data."
}