@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Automated Telescope-Paper Linkage via Multi-Model Ensemble Learning
%A Ojaswa Varshney, Ojaswa
%A Vyas, Prashasti
%A Goyal, Priyanka
%A Singh, Tarpita
%A Kumar, Ritesh
%A Singh, Mayank
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%S Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India and virtual
%@ 979-8-89176-310-4
%F ojaswa-varshney-etal-2025-automated
%X 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.
%U https://aclanthology.org/2025.wasp-main.15/
%P 127-135
Markdown (Informal)
[Automated Telescope-Paper Linkage via Multi-Model Ensemble Learning](https://aclanthology.org/2025.wasp-main.15/) (Ojaswa Varshney et al., WASP 2025)
ACL
- Ojaswa Ojaswa Varshney, Prashasti Vyas, Priyanka Goyal, Tarpita Singh, Ritesh Kumar, and Mayank Singh. 2025. Automated Telescope-Paper Linkage via Multi-Model Ensemble Learning. In Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications, pages 127–135, Mumbai, India and virtual. Association for Computational Linguistics.