edoardosignoroni/eff_archs_lowre

Results and code for the paper "Efficient Architetures for Low-resource Machine Translation" (Workshop on Advancing NLP for Low-Resource Languages at RANLP 2025 (Varna, Bulgaria), Sep 13) # Efficient Architectures for Low-Resource Machine Translation Low-resource Neural Machine Translation is highly sensitive to hyperparameters and needs careful tuning to achieve the best results with small amounts of training data. We focus on exploring the impact of changes in the Transformer architecture on downstream translation quality, and propose a metric to score the computational efficiency of such changes. By experimenting on English-Akkadian, German-Lower Sorbian, English-Italian, and English-Manipuri, we confirm previous finding in low-resource machine translation optimization, and show that smaller and more parameter-efficient models can achieve the same translation quality of larger and unwieldy ones at a fraction of the computational cost. We compile a list of optimal ranges for each hyperparameter. ## Citation: