A Statistically Validated Bidirectional Neural Machine Translation Framework for the Low-Resource Tigrigna–Kunama Language Pair

This repository contains the official dataset and experimental codebase for the study "A Statistically Validated Bidirectional Neural Machine Translation Framework for the Low-Resource Tigrigna–Kunama Language Pair." The dataset includes: An expert-verified parallel corpus of 4,712 sentence pairs. Original, authentic Ge'ez script morphology preserved without homophone normalization. The codebase includes: A unified joint-bidirectional Bi-LSTM architecture with Luong global attention. Preprocessing pipelines, BPE subword modeling (4,000 units), and automated training/evaluation scripts. Statistical validation protocols (paired bootstrap resampling, 5-fold cross-validation).