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

The design, construction, and assessment of a compact Transformer-based Neural Machine Translation (NMT) system for English–Kibajuni translation suggest that appropriately scaled Transformer models, paired with subword tokenization and carefully tuned training procedures, can materially advance digital inclusion and la...

RAÍZ: A Hybrid Transformer-Based Machine Translation Architecture with Linguistic Preprocessing and Community Validation for Low-Resource Indigenous Languages (Spanish – Nahuatl and Spanish – Mazahua)

Democratizing Machine Translation: A CPU-Centric Training Pipeline for Low-Resource Languages (A Kalabari Case Study)

The results demonstrate a reproducible, CPU-centric pipeline, proving that the lack of specialized GPU infrastructure is not an insurmountable obstacle for digital language preservation and baseline NMT development.

Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study

This paper proposes a general optimization framework that combines a vocabulary pruning method with a targeted fine-tuning protocol for MNMT models, and reduces the vocabulary size from over 128,000 to approximately 10,000 tokens, enabling a 60% memory saving without any loss in performance.

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...

Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation

The results show that multilingual transfer is the dominant factor in extremely low-resource Bantu translation while eliminating the need for heuristic proxy selection, and all systems fail to preserve tonal diacritics, highlighting an open challenge.