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        <title>Directed Attention is All You Need: Profiling Style from Limited Text Data</title>
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            <title>Proceedings of the Second Workshop Natural Language Processing for Turkic Languages (SIGTURK 2026)</title>
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            <namePart type="given">Kemal</namePart>
            <namePart type="family">Oflazer</namePart>
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            <namePart type="family">Köksal</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Directed Attention is All You Need: Profiling Style from Limited Text Data
%A Akdağ, Hüseyin Emir
%Y Oflazer, Kemal
%Y Köksal, Abdullatif
%Y Varol, Onur
%S Proceedings of the Second Workshop Natural Language Processing for Turkic Languages (SIGTURK 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-370-8
%F akdag-2026-directed
%X 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.
%U https://aclanthology.org/2026.sigturk-1.2/
%P 14-27

Markdown (Informal)

[Directed Attention is All You Need: Profiling Style from Limited Text Data](https://aclanthology.org/2026.sigturk-1.2/) (Akdağ, SIGTURK 2026)

• Directed Attention is All You Need: Profiling Style from Limited Text Data (Akdağ, SIGTURK 2026)

ACL

• Hüseyin Emir Akdağ. 2026. Directed Attention is All You Need: Profiling Style from Limited Text Data. In Proceedings of the Second Workshop Natural Language Processing for Turkic Languages (SIGTURK 2026), pages 14–27, Rabat, Morocco. Association for Computational Linguistics.