@inproceedings{palen-michel-etal-2021-seqscore,
    title = "{S}eq{S}core: Addressing Barriers to Reproducible Named Entity Recognition Evaluation",
    author = "Palen-Michel, Chester  and
      Holley, Nolan  and
      Lignos, Constantine",
    editor = "Gao, Yang  and
      Eger, Steffen  and
      Zhao, Wei  and
      Lertvittayakumjorn, Piyawat  and
      Fomicheva, Marina",
    booktitle = "Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.eval4nlp-1.5/",
    doi = "10.18653/v1/2021.eval4nlp-1.5",
    pages = "40--50",
    abstract = "To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around transparency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures."
}