@inproceedings{harbecke-etal-2022-micro,
    title = "Why only Micro-F1? Class Weighting of Measures for Relation Classification",
    author = "Harbecke, David  and
      Chen, Yuxuan  and
      Hennig, Leonhard  and
      Alt, Christoph",
    editor = "Shavrina, Tatiana  and
      Mikhailov, Vladislav  and
      Malykh, Valentin  and
      Artemova, Ekaterina  and
      Serikov, Oleg  and
      Protasov, Vitaly",
    booktitle = "Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.nlppower-1.4/",
    doi = "10.18653/v1/2022.nlppower-1.4",
    pages = "32--41",
    abstract = "Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes. We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model."
}