@inproceedings{van-der-goot-etal-2024-enough,
    title = "Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using {U}niversal {D}ependencies",
    author = {van der Goot, Rob  and
      Liu, Zoey  and
      M{\"u}ller-Eberstein, Max},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.544",
    pages = "6167--6176",
    abstract = "When creating a new dataset for evaluation, one of the first considerations is the size of the dataset. If our evaluation data is too small, we risk making unsupported claims based on the results on such data. If, on the other hand, the data is too large, we waste valuable annotation time and costs that could have been used to widen the scope of our evaluation (i.e. annotate for more domains/languages). Hence, we investigate the effect of the size and a variety of sampling strategies of evaluation data to optimize annotation efforts, using dependency parsing as a test case. We show that for in-language in-domain datasets, 5,000 tokens is enough to obtain a reliable ranking of different parsers; especially if the data is distant enough from the training split (otherwise, we recommend 10,000). In cross-domain setups, the same amounts are required, but in cross-lingual setups much less (2,000 tokens) is enough.",
}

When creating a new dataset for evaluation, one of the first considerations is the size of the dataset. If our evaluation data is too small, we risk making unsupported claims based on the results on such data. If, on the other hand, the data is too large, we waste valuable annotation time and costs that could have been used to widen the scope of our evaluation (i.e. annotate for more domains/languages). Hence, we investigate the effect of the size and a variety of sampling strategies of evaluation data to optimize annotation efforts, using dependency parsing as a test case. We show that for in-language in-domain datasets, 5,000 tokens is enough to obtain a reliable ranking of different parsers; especially if the data is distant enough from the training split (otherwise, we recommend 10,000). In cross-domain setups, the same amounts are required, but in cross-lingual setups much less (2,000 tokens) is enough.
van der Goot, Rob Liu, Zoey M{\"u}ller-Eberstein, Max
Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using Universal Dependencies