Lexical borrowing, the adoption of words from one language into another, is a ubiquitous linguistic phenomenon influenced by geopolitical, societal, and technological factors. This paper introduces ConLoan–a novel contrastive dataset comprising sentences with and without loanwords across 10 languages. Through systematic evaluation using this dataset, we investigate how state-of-the-art machine translation and language models process loanwords compared to their native alternatives. Our experiments reveal that these systems show systematic preferences for loanwords over native terms and exhibit varying performance across languages. These findings provide valuable insights for developing more linguistically robust NLP systems.

In the growing domain of natural language processing, low-resourced languages like Northern Kurdish remain largely unexplored due to the lack of resources needed to be part of this growth. In particular, the tasks of part-of-speech tagging and tokenization for Northern Kurdish are still insufficiently addressed. In this study, we aim to bridge this gap by evaluating a range of statistical, neural, and fine-tuned-based models specifically tailored for Northern Kurdish. Leveraging limited but valuable datasets, including the Universal Dependency Kurmanji treebank and a novel manually annotated and tokenized gold-standard dataset consisting of 136 sentences (2,937 tokens). We evaluate several POS tagging models and report that the fine-tuned transformer-based model outperforms others, achieving an accuracy of 0.87 and a macro-averaged F1 score of 0.77. Data and models are publicly available under an open license at https://github.com/peshmerge/northern-kurdish-pos-tagging