@inproceedings{ignatev-etal-2026-dataset,
    title = "Dataset Cartography for Implicit Discourse Relation Recognition: Promises and Pitfalls",
    author = "Ignatev, Daniil  and
      Paperno, Denis  and
      Poesio, Massimo",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Ogrodniczuk, Maciej  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Nov{\'a}k, Michal  and
      Li, Chuyuan  and
      Strube, Michael  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference ({CODI}-{CRAC} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.codi-1.8/",
    pages = "53--64",
    ISBN = "979-8-89176-400-2",
    abstract = "Crowdsourced data for implicit discourse relation recognition, IDRR, has been shown to contain both plausible interpretations and noisy annotations. We present a case study of dataset cartography (Swayamdipta 2020) on IDRR-focused DiscoGeM corpus (Scholman et al., 2022). Our findings show that error identification via low confidence proves unreliable, as confidence is strongly affected by label rarity. However, high-confidence datapoints reveal a different use case: auditing the cue-rich regions of the dataset. Our lexical probe demonstrates an association between high confidence items and (mostly temporal) intra-argument cue words. Dataset cartography can thus serve a diagnostic of cue-driven easy-to-learn cases, which need to be balanced out to ensure the robustness of IDRR learning."
}