Transformer Encoders with Heuristic-Guided Contrastive Learning for Software Coreference Resolution

Abstract

This paper describes our system submitted to the Software Mention Detection and Coreference Resolution (SOMD) 2026 shared task, specifically for Subtask 1 (cross-document coreference resolution over gold-standard mentions) and Subtask 2 (cross-document coreference resolution over predicted mentions). The proposed approach employs a SciBERT architecture trained with Supervised Contrastive (SupCon) loss to generate dense mention representations, which are then clustered using Hierarchical Agglomerative Clustering (HAC) with average linkage. Software-aware heuristics are integrated to exploit domain-specific signals such as software name canonicalization and developer disambiguation to adjust pairwise similarity scores before clustering. The system achieved strong performance, with a CoNLL F1 score of 92.18% on coreference resolution over gold-standard mentions and 91.87% on coreference resolution over predicted mentions, showing significant performance of our approach in this area for human annotated and automated systems respectively

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