@inproceedings{gebesce-etal-2026-overview,
    title = "Overview of the {SIGTURK} 2026 Shared Task: Terminology-Aware Machine Translation for {E}nglish{--}{T}urkish Scientific Texts",
    author = {Gebe{\c{s}}{\c{c}}e, Ali  and
      Safa, Abdulfattah  and
      Amasya, Ege U{\u{g}}ur  and
      {\c{S}}ahin, G{\"o}zde G{\"u}l},
    editor = {Oflazer, Kemal  and
      K{\"o}ksal, Abdullatif  and
      Varol, Onur},
    booktitle = "Proceedings of the Second Workshop Natural Language Processing for {T}urkic Languages ({SIGTURK} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.sigturk-1.20/",
    pages = "236--247",
    ISBN = "979-8-89176-370-8",
    abstract = "This paper presents an overview of the SIGTURK 2026 Shared Task on Terminology-Aware Machine Translation for English-Turkish Scientific Texts. We address the critical challenge of terminological accuracy in low-resource settings by constructing the first terminology-rich English-Turkish parallel corpus, comprising 3,300 sentence pairs from STEM domains with 10,157 expert-validated term pairs. The shared task consists of three subtasks: term detection, expert-guided correction, and end-to-end post-editing. We evaluate state-of-the-art baselines (including GPT-5.2 and Claude Sonnet 4.5) alongside participant systems employing diverse strategies from fine-tuning to Retrieval-Augmented Generation (RAG). Our results highlight that while massive generalist models dominate zero-shot detection, smaller, domain-adapted models using Supervised Fine-Tuning and Reinforcement Learning can significantly outperform them in end-to-end post-editing. Furthermore, we find that rigid retrieval pipelines often disrupt fluency, whereas Chain-of-Thought prompting allows models to integrate terminology more naturally. Despite these advances, a significant gap remains between automated systems and human expert performance in strict terminology correction."
}