nmt

The paper benchmarks Arabic–Russian machine translation by comparing fine-tuned NMT models and few-shot LLMs, finding that fine-tuned NMT significantly outperforms LLMs under low-resource conditions.

This paper presents a unified poly-dialectal neural machine translation system for 12 Bangla regional dialects, introducing the largest multi-dialect parallel corpus to date and achieving state-of-the-art BLEU scores with a fine-tuned BanglaT5 model using DoRA.

This paper proposes a hybrid pipeline combining fine-tuned LLaMA-based gender classification with tag-aware neural machine translation to mitigate gender bias in English-to-Romanian MT, introducing new datasets and improving gender accuracy by over 40 points on benchmarks.

TabletCraft is an open-source system enabling bidirectional Akkadian-English neural machine translation with cuneiform rendering, allowing users to both read ancient tablets and compose new messages in cuneiform. Accepted to the C3NLP workshop at ACL 2026, it reports first published quantitative results for English-to-Akkadian translation.

Presents a neural machine translation system for the severely under-resourced Tangkhul–English language pair, achieving strong BLEU, chrF++, BERTScore, and COMET scores using fine-tuned ByT5-large and mT5-small models.

This paper presents PiDA, a phonetically-informed data augmentation method for Vietnamese speech translation that improves robustness by generating ASR-like corruptions using phonetic word embeddings, achieving up to +2.04 BLEU on noisy outputs.

Compares DeepL, Gemini with basic prompt, and Gemini with glossary-augmented prompting for translating rock art Spanish-English terminology, finding that glossary-augmented prompting achieves the highest terminology accuracy (81.4%).