Nöral Makine Çevirisinde Plastisite: Bilişsel ve Teknik bir Perspektif

Yıl 2025, Sayı: 23, 73 - 93, 11.12.2025

Derya Oğuz

https://doi.org/10.26650/iujts.2025.1711530

https://izlik.org/JA64NS69EJ

Öz

Nöroplastisite, insan beyninin değişken koşullara adapte olması ve uyum sağlaması konusundaki esnekliği ifade eden bir kavramdır. Yapay zekâ sistemlerinde ve nöral makine çevirisinde ise plastisite, çeviri çıktılarının doğruluğu ve bağlama duyarlılığı ile ilgili bir konudur. Bu çalışma, nöral makine çevirisinde plastisitenin nasıl sağlandığını ve bunu sağlayan mekanizmaların çeviri sürecindeki etkisini incelemeyi amaçlamaktadır. Plastisite olgusu, çalışmada veri temelli kavramsal analiz yaklaşımıyla ele alınmış, teknik boyutunun yanı sıra erek dilde anlamın yeniden yapılandırılması ve bağlamsal esnekliğin işlevselliği bakımından incelenmiştir. Bu doğrultuda dil modellerinde plastisiteyi sağlayan iki temel mekanizma ele alınmıştır: Birincisi hataya dayalı öğrenme süreci olan ve geri yayılım anlamına gelen backpropagation, ikincisi ise bir dil modelinin kullanıcı tarafından yeni girdilere uyumlanmasını içeren fine-tuning işlemi. Bu işleyişin arkasındaki mekanizma ise attention terimiyle ifade edilmektedir. Bu mekanizmalar öncelikle teknik özellikleriyle alanyazında araştırılmış, makine öğrenmesindeki plastisitenin insan beynindeki plastisite kavramından farkı ifade edilmiştir. Ardından bu mekanizmaların işlevselliği, örnek metinler, dikkat hari taları ve eğitilen bir dil modeli üzerinden yürütülen doküman analizi kapsamında betimleyici bir biçimde analiz edilmiştir. Elde edilen bulgular çeviribilimsel bakış açısından bilişsel süreçlerle ilişkilendirilmiş, model çıktılarının bağlamsal esneklik kapasitesi incelenmiştir. Bulgular, yapay öğrenme sistemlerinin nöral makine çevirisi yaparken yüzeysel anlam eşleştirmede başarılı olduğunu, bununla birlikte modelin işleyişinde istenmeyen yan etkilere de yol açabileceğini göstermiştir.

Anahtar Kelimeler

dikkat , ince ayar , geri yayılım , nöral makine çevirisi , plastisite

Plasticity in Neural Machine Translation: A Cognitive and Technical Perspective

https://izlik.org/JA64NS69EJ

Öz

Neuroplasticity refers to the flexibility of the human brain in adapting to changing conditions. In artificial intelligence systems and neural machine translation (NMT), plasticity is related to the accuracy and contextual sensitivity of the translation outputs. This study aims to explore how plasticity is achieved in NMT and how its underlying mechanisms affect the translation process. The phenomenon is addressed through a data-driven conceptual analysis, focusing not only on its technical dimension but also on meaning reconstruction in the target language and the functional role of contextual flexibility. In this regard, two core mechanisms enabling plasticity are examined: backpropagation, an error-based learning process, and fine-tuning, which involves adapting a language model to new input by users. The underlying operation is driven by attention mechanisms. These processes are f irst investigated through the technical literature, highlighting how machine learning plasticity differs from human neuroplasticity. Then, their functionality is analyzed descriptively through a document analysis involving sample texts, attention maps, and an adapted language model. The findings are associated with cognitive processes from a translation studies perspective, showing that while NMT performs well in surface-level meaning matching, it may also produce unintended side effects within its operational logic.

Anahtar Kelimeler

attention , backpropagation , fine-tuning , neural machine translation , plasticity

Kaynakça

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