@inproceedings{ojaswa-varshney-etal-2025-automated,
    title = "Automated Telescope-Paper Linkage via Multi-Model Ensemble Learning",
    author = "Ojaswa Varshney, Ojaswa  and
      Vyas, Prashasti  and
      Goyal, Priyanka  and
      Singh, Tarpita  and
      Kumar, Ritesh  and
      Singh, Mayank",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.15/",
    pages = "127--135",
    ISBN = "979-8-89176-310-4",
    abstract = "Automated linkage between scientific publications and telescope datasets is a cornerstone for scalable bibliometric analyses and ensuring scientific reproducibility in astrophysics. We propose a multi-model ensemble architecture integrating transformer models DeBERTa, RoBERTa, and TF-IDF logistic regression, tailored to the WASP-2025 shared task on telescope-paper classification. Our approach achieves a macro F1 score approaching 0.78 after extensive multi-seed ensembling and per-label threshold tuning, significantly outperforming baseline models. This paper presents comprehensive methodology, ablation studies, and an in-depth discussion of challenges, establishing a robust benchmark for scientific bibliometric task automation."
}