Morphological and structural complexity analysis of low-resource English-Turkish language pair using neural machine translation models.

ABSTRACT: Neural machine translation (NMT) has achieved remarkable success in high-resource language pairs; however, its effectiveness for morphologically rich and low-resource languages like Turkish remains underexplored. As a highly agglutinative and morphologically complex language with limited high-quality parallel data, Turkish serves as a representative case for evaluating NMT systems on low-resource and linguistically challenging settings. Its structural divergence from English makes it a critical testbed for assessing tokenization strategies, attention mechanisms, and model generalizability in neural translation. This study investigates the comparative performance of two prominent NMT paradigms-the Transformer architecture, and recurrent-based sequence-to-sequence (Seq2Seq) models with atten

SUBMITTER: Acı M 

PROVIDER: S-EPMC12453858 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature