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September 10, 2025Open Access

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

Key Points

The Transformer model achieved the highest BLEU score of 47.85 for English-to-Turkish translation, demonstrating its effectiveness.
Using different tokenization strategies, the study compared Transformer and seq2seq models in translating between English and Turkish.
The research confirmed that optimized neural machine translation systems can handle the complexity of morphologically rich languages.
Findings indicate that the success of translation systems is achievable even with limited parallel data in low-resource settings.

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 attention for both English-to-Turkish and Turkish-to-English translation. The models are evaluated under various configurations, including different tokenization strategies (Byte Pair Encoding (BPE) vs . Word Tokenization), attention mechanisms (Bahdanau and an exploratory hybrid mechanism combining Bahdanau and Scaled Dot-Product attention), and architectural depths (layer count and attention head number). Extensive experiments using automatic metrics such as BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Translation Error Rate (TER) reveal that the Transformer model with three layers, eight attention heads, and BPE tokenization achieved the best performance, obtaining a BLEU score of 47.85 and METEOR score of 44.62 in the English-to-Turkish direction. Similar performance trends were observed in the reverse direction, indicating the model’s generalizability. These findings highlight the potential of carefully optimized Transformer-based NMT systems in handling the complexities of morphologically rich, low-resource languages like Turkish in both translation directions.

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