[2604.21469] Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection

Computer Science > Computation and Language arXiv:2604.21469 (cs) [Submitted on 23 Apr 2026] Title:Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection Authors:Fariz Ikhwantri, Dusica Marijan View a PDF of the paper titled Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection, by Fariz Ikhwantri and 1 other authors View PDF HTML (experimental) Abstract:Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one regulation often fail to generalise to others. This limitation underscores the need for principled methods to improve cross-domain transfer. We study data selection as a strategy to mitigate negative transfer in compliance detection framed as a natural language inference (NLI) task. Specifically, we evaluate four approaches for selecting augmentation data from a larger source domain: random sampling, Moore-Lewis's cross-entropy difference, importance weighting, and embedding-based retrieval. We systematically vary the proportion of selected data to analyse its effect on cross-domain adaptation. Our findings demonstrate that targeted data selection substantially reduces negative transfer, offering a practical path toward scalable and reliable compliance automation across heterogeneous regulations. Comments: Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2604.21469 [cs.CL]   (or arXiv:260...