arXiv Computation and Language October 23, 2026 By Donato Meoli, Eryk Wdowiak

Climbing the Ladder: Automating Data Pipelines and Reproducing Baselines in Low-Resource Settings

Read the original on arXiv Computation and Language →

The paper describes the redevelopment of a small neural machine translation system for Sicilian, using parallel text extracted from the bilingual journal Arba Sicula and a language‑agnostic sentence encoder for alignment. It compares training small Transformers from scratch with a pretrained NLLB model fine‑tuned via low‑rank adapters, and applies a staged reverse‑training recipe that improves translation into Sicilian more than from it. Results show the pretrained model outperforms the small Transformers, but a multilingual adapter can degrade performance on untrained language directions.

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