Dictionaries play an important role in neural machine translation (NMT). However, a large dictionary requires a significant amount of memory, which limits the application of NMT and can cause a memory error. This limitation can be solved by segmenting each word into morphemes in parallel source corpora. Therefore, this study introduces a new morphological segmentation approach for Turkic languages based on the complete set of endings (CSE), which reduces the vocabulary volume of the source corpora. Herein, we demonstrate the proposed CSE-based morphological segmentation method for the Kazakh, Kyrgyz, and Uzbek languages and present the results of computational NMT experiments for the Kazakh language. The NMT experiment results show that in comparison with byte-pair encoding (BPE)-based segmentation, the proposed CSE-based segmentation increases the bilingual evaluation understudy score of 0.5 and 0.2 points on average for Kazakh–English and English–Kazakh pairs, respectively. Furthermore, in comparison with the BPE-based segmentation, the proposed CSE-based segmentation approach reduced the vocabulary size in NMT by more than a factor of two. This feature of the proposed segmentation approach will be crucial for NMT as the size of the source corpora is increased to improve translation quality.

This study presents the application of free, open-source artificial intelligence (AI) techniques to advance machine translation for low-resource Turkic languages such as Kazakh, Azerbaijani, Kyrgyz, Turkish, Turkmen, and Uzbek. This machine translation problem for Turkic languages is part of a project to generate meeting minutes from speech transcripts. Due to limited parallel corpora and underdeveloped linguistic tools for these languages, traditional machine translation approaches often underperform. The goal is to reduce digital inequality for these languages and to support scalability. We investigate the effectiveness of free open-source pre-trained specialized and general-purpose AI models for morphologically rich state Turkic languages. This research includes developing parallel corpora for six Turkic languages, fine-tuning, and performance evaluation using BLEU, WER, TER, and chrF metrics. The parallel corpora for five pair languages, each of 300,000 and 500,000 sentences, were generated and cleaned. The results for corpora 500,000 parallel sentences show significant improvements compared with baseline NLLB-200 1.3B on average: BLEU increased by 23.81 points, chrF increased by 26.05 points, and WER and TER decreased by 0.36 and 33.95, respectively, after cleaning and fine-tuning. Six Turkic-language multilingual parallel corpora of 3 885 542 sentences were developed and the fine-tuning of NLLB-200 1.3B shows the following, compared with the results for 500,000 cleaned corpus: BLEU increased by 4.3 points, chrF increased by 1.7 points, and WER and TER decreased by 0.1 and 4.75, respectively These results demonstrate the high efficiency of corpus cleaning and synthetic data generation to improve the quality of machine translation for low-resource Turkic languages using AI models. These results were confirmed by external evaluation on the FLORES 200 dataset and human evaluation. The scientific contribution of this article is the development of a methodology for generating parallel corpora using a specialized AI model of machine translation and fine-tuning the specialized AI model on the created corpora, creating new multilingual parallel corpora of Azerbaijan–Kazakh, Kyrgyz–Kazakh, Turkish–Kazakh, Turkmen–Kazakh, and Uzbek–Kazakh pairs using the proposed methodology, cleaning them, and conducting fine-tuning experiments.