From In-Distribution to Out-of-Distribution: Joint Loss for Improving Generalization in Software Mention and Relation Extraction

Identifying software entities and their semantic relations in scientific texts contributes to improved reproducibility and allows for the construction machine-readable knowledge graphs.However, 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-distribution 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 outof-distribution robustness compared to disjoint training.Our results reveal significant performance gaps between in-and out-of-distribution 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 (indistribution) in the SOMD 2025 shared task, showing strong generalization and robust performance across domains.All code is publicly available. 1