Automated Multilingual Translation Using Neural Machine Translation and Transformer Architecture

Automated Multilingual Translator Using Neural Translation

The results indicate that a moderately sized, shared self-attention architecture can deliver production-quality multilin-gual translation within the resource constraints of an academic de-ployment, while surfacing clear directions – low-resource language coverage, domain adaptation, and speech-based extension – for con-tinued development.

Building a Transformer-Based Neural Machine Translation System for English–Kibajuni Translation: A Low-Resource Deep Learning Approach for Indigenous Language Preservation

Building ‌a ‌Transformer-Based ‌Neural Machine Translation System for English–Kibajuni Translation: A Low-Resource Deep Learning Approach for Indigenous Language Preservation

Recent progress in artificial intelligence has pushed machine translation to high levels of accuracy for widely resourced languages. Yet for many indigenous and endangered languages, comparable tools remain absent, largely because digitized linguistic data are scarce. Kibajuni, a minimally documented Bantu language spoken along the Kenyan coast, illustrates this gap. Publicly accessible English–Kibajuni machine translation systems are not available, which restricts both everyday digital use and broader language preservation work.This paper reports the design, construction, and assessment of a compact Transformer-based Neural Machine Translation (NMT) system for English–Kibajuni translation. Training relied on a community-produced parallel corpus of roughly 10,000 aligned sentence pairs. A Design Science Research approach guided development of the full translation workflow, beginning with corpus preparation and continuing through Byte Pair Encoding (BPE) tokenization, a custom encoder–decoder Transformer, supervised training in PyTorch, beam-search decoding at inference time, and deployment as a web application.Because data were limited, emphasis was placed on training stability and generalization rather than increasing model size. The system therefore integrated AdamW, OneCycle learning-rate scheduling, dropout, label smoothing, gradient clipping, mixed-precision training, and early stopping driven by validation BLEU. Results indicate that, despite the small dataset, the model learned usable semantic correspondences between English and Kibajuni while remaining computationally light. The final network contains about 6–8 million parameters, occupies roughly 27 MB, and supports real-time translation on modest hardware. In practical terms, the work provides one of the earliest operational English–Kibajuni neural translation platforms. At the methodological level, it offers a reproducible template for developing MT systems for other under-resourced African languages. Taken together, the findings suggest that appropriately scaled Transformer models, paired with subword tokenization and carefully tuned training procedures, can materially advance digital inclusion and language preservation for endangered languages. 

Development of a Transformer-Based Neural Machine Translation System for English to Ebira Language

Transformer-based neural machine translation (NMT) models have boosted translation accuracy for high-resource languages; however, research has largely bypassed unwritten and low-resource languages, particularly African languages such as Ebira. Ebira is an unwritten, low-resource language spoken by approximately 2.5 million people predominantly in Kogi State, Nigeria. Existing Ebira machine translation (MT) systems suffer from poor fluency, accuracy, and missed nuances, constrained by small datasets and rule-based methods. This study presents the development of a neural machine translation (NMT) system for English-to-Ebira translation using Google’s T5-base transformer model. A bilingual parallel corpus of 32,322 English-Ebira sentence pairs was compiled and used to fine-tune the model. The system achieved a corpus-level BLEU score of 40.95%, with 87% of evaluated sentences scoring 0.5 BLEU or higher, surpassing the prior rule-based system’s threshold result of 81.50%, corresponding to 6.75% relative improvement. Human evaluation by ten native Ebira speakers yielded a mean rating of 8.33/10 for fluency, accuracy, and cultural relevance. This research demonstrated that the application of transfer learning on transformer NMT model significantly improves the quality of (MT) systems; and also provides a foundational step for the development of computational resources for Ebira and supports the broader goal of linguistic inclusivity in artificial intelligence.

Dynamic semantic fusion based transformer modelling for automatic translation of Chinese–English languages using deep embeddings

Chinese English neural machine translation remains challenging due to substantial syntactic divergence, word-order variation, lexical ambiguity, and cross-lingual semantic mismatch. These challenges often lead to semantic omissions, over-translation, and weak source–target semantic alignment in transformer-based translation systems. Although transformer architecture has achieved remarkable progress, they frequently exhibit limitations in capturing complementary lexical and sentence-level semantic information while offering limited interpretability of the translation process. To address these challenges, this paper proposes DSF–MarianMT, a semantic fusion enhanced neural machine translation framework built upon MarianMT. The proposed framework integrates word-level and sentence-level semantic representations through a dynamic semantic fusion mechanism and employs a contrastive semantic learning objective to improve source–target semantic consistency during training. Experimental evaluation on a Chinese English translation dataset demonstrates the effectiveness of the proposed approach, achieving 36.5 BLEU, 60.2 chrF, 39.8 TER, and a COMET score of 0.78, outperforming the standard MarianMT baseline across all evaluation metrics. In addition to improved translation quality, interpretability analyses reveal reduced attention entropy, lower redundancies among attention heads, and stable token-level semantic learning. Furthermore, error analysis indicates fewer semantic omissions and over-translation errors, while consistent performance is maintained across sentences of varying lengths. These findings demonstrate that the proposed framework effectively enhances semantic representation learning and translation fidelity for Chinese–English neural machine translation.

Using English-Based NLP Tools for Domain-Specific Text in Foreign Languages

Social scientists often machine-translate foreign-language texts into English and apply English-based natural language processing tools without systematically evaluating translation quality or annotation efficiency. To address this problem, this study provides evidence-based guidance for researchers applying English-centric natural language processing to domain-specific foreign-language corpora. We provide and empirically validate a structured framework combining multi-system machine translation evaluation and active learning for domain-specific text classification. Using 11,493 parallel Spanish and Arabic sentences aligned to English, we compare four machine translation systems (Google Translate, Deep, DeepL, OPUS) using SacreBLEU, METEOR, COMET, and BERTScore quality scores. Across languages and metrics, machine translation systems yield statistically comparable performance. We then evaluate eight active learning strategies using ConfliBERT for political conflict classification under a 20% annotation budget, corresponding to 1,155 samples from the training split. Binary classification exceeds F1 = 0.90, while QuadClass multi-class performance peaks around F $1~\approx ~0.75$ . The Ensemble Intersection strategy achieves the highest performance in 53% of tasks and often matches or surpasses full-dataset results using only a fraction of labeled data. These results provide a practical workflow for researchers using English-based natural language processing tools on foreign-language, domain-specific corpora.