@inproceedings{mandic-etal-2025-distribution,
    title = "From In-Distribution to Out-of-Distribution: Joint Loss for Improving Generalization in Software Mention and Relation Extraction",
    author = "Mandic, Stasa  and
      Niess, Georg  and
      Kern, Roman",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sdp-1.14/",
    doi = "10.18653/v1/2025.sdp-1.14",
    pages = "146--153",
    ISBN = "979-8-89176-265-7",
    abstract = "Identifying software entities and their semantic relations in scientific texts is key for reproducibility and machine-readable knowledge graphs, yet models struggle with domain variability and sparse supervision. We address this by evaluating joint Named Entity Recognition (NER) and Relation Extraction (RE) models on the SOMD 2025 shared task, emphasizing generalization to out-of-domain scholarly texts. We propose a unified training objective that jointly optimizes both tasks using a shared loss function and demonstrates that joint loss formulations can improve out-of-domain robustness compared to disjoint training. Our results reveal significant performance gaps between in- and out-of-domain settings, prompting critical reflections on modeling strategies for software knowledge extraction. Notably, our approach ranked 1st in Phase 2 (out-of-distribution) and 2nd in Phase 1 (in-distribution) in the SOMD 2025 shared task, showing strong generalization and robust performance across domains."
}