Authorial style transfer is particularly challenging in low-resource scenarios, such as those presented by languages with a distinct socio-digital trajectory like Turkish, where contemporary digital text coexists with under-resourced literary and historical styles. This work addresses this gap through the Dual-Stage Stylometric Imprinting (DSSI) framework, introducing a Rule+Example paradigm for effective style profiling. Evaluated on a corpus of Turkish texts, the approach enables smaller models to achieve up to 90% of large model performance by combining explicit stylistic guidelines with contextual demonstrations. The findings demonstrate altered scaling laws for stylistic tasks and facilitate the practical deployment of personalized style transfer for preserving distinctive writing characteristics.

We introduce TurCOMET, the first Turkish-specific augmentation of COMET for evaluating translations between Turkish and English. By extending xCOMET-XL with Turkish morphological features and training it on additional Turkish-English data via an additive bottleneck layer, TurCOMET resolves neural evaluation failures caused by sub-word over-segmentation. To validate our metric, we introduce TurkDoc-MT, a meticulously curated Turkish-English document-level aligned dataset of 1,322 instances spanning seven domains. Meta-evaluation demonstrates that TurCOMET significantly outperforms the off-the-shelf baseline, yielding absolute Kendall’s τ correlation gains of +0.113 (system-level) and +0.024 (segment-level), with the largest gains in the TR→EN direction. All models, datasets, code, and annotations are publicly released for the benefit of Turkish natural language processing and benchmarking any translation system.