Misbranding of health-related products poses significant risks to public safety and regulatory compliance. Existing approaches to claim verification largely rely on keyword matching or generic text classification, failing to capture the nuanced reasoning required to align product claims with legal statutes. In this work, we introduce RegNLI, a novel framework that formulates misbranding detection as a inference task between product claims and regulatory provisions. Leveraging a curated dataset of FDA warning letters, we construct structured representations of claims and statutes. Our model integrates a regulation-aware gating mechanism with a contrastive alignment objective to jointly optimize misbranding classification and statute mapping. Experiments on the FDA-Misbrand dataset demonstrate that RegNLI significantly outperforms strong baselines across accuracy, F1-score, and regulation alignment metrics, while providing interpretable attention patterns that highlight critical linguistic cues. This work establishes a foundation for compliance-aware NLP systems and opens new directions for integrating formal reasoning with neural architectures in regulatory domains.

This paper presents a cross-linguistic analysis of phonological similarity in sign languages using symbolic representations from the Hamburg Notation System (HamNoSys). We construct a dataset of 1000 signs each from British Sign Language (BSL), German Sign Language (DGS), French Sign Language (LSF), and Greek Sign Language (GSL), and compute pairwise phonological similarity using normalized edit distance over HamNoSys strings. Our analysis reveals both universal and language-specific patterns in handshape usage, movement dynamics, non-manual features, and spatial articulation. We explore intra and inter-language similarity distributions, phonological clustering, and co-occurrence structures across feature types. The findings offer insights into the structural organization of sign language phonology and highlight typological variation shaped by linguistic and cultural factors.

In this paper we propose a framework for automatic translation of English text to American Sign Language (ASL) which leverages a linguistically informed transformer model to translate English sentences into ASL gloss sequences. These glosses are then associated with respective ASL videos, effectively representing English text in ASL. To facilitate experimentation, we create an English-ASL parallel dataset on banking domain.Our preliminary results demonstrated that the linguistically informed transformer model achieves a 97.83% ROUGE-L score for text-to-gloss translation on the ASLG-PC12 dataset. Furthermore, fine-tuning the transformer model on the banking domain dataset yields an 89.47% ROUGE-L score when fine-tuned on ASLG-PC12 + banking domain dataset. These results demonstrate the effectiveness of the linguistically informed model for both general and domain-specific translations. To facilitate parallel dataset generation in banking-domain, we choose ASL despite having limited benchmarks and data corpus compared to some of the other sign languages.