Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs
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EnSiTa is a trilingual multi‑domain parallel dataset and benchmark for English, Sinhala, and Tamil. It contains human post‑edited training data across seven domains and professionally translated test sets for those domains plus an additional one, all produced through a multi‑year, rigorously quality‑controlled process. The authors use EnSiTa to conduct a comprehensive study of domain‑specific machine translation across six language directions, comparing from‑scratch Transformers, pre‑trained models, and decoder‑only LLMs under various training‑data sizes, model scales, and domain settings.
arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.
arXiv:2606. 25365v2 Announce Type: replace-cross Abstract: We present a study on low-resource machine translation for the Tangkhul-English (nmf-en) language pair.
The study evaluates Arabic–Russian machine translation by comparing seven fine‑tuned neural machine translation (NMT) models with four few‑shot large language models (LLMs) on a new 15.47 million‑pair corpus split into 20k/5k/5k. Fine‑tuned NLLB‑1.3B achieves the best performance (BLEU 16.3, COMET 0.738), while the best few‑shot LLM, Aya‑Expanse 8B, scores only BLEU 1.7 on 500 sentences. Error analysis shows that low lexical overlap between Arabic and Russian is the main source of failures, and statistical tests confirm significant performance gaps between most models.