arXiv:2609.18529v1 Announce Type: new Abstract: UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is unc...
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.
arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.
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.
arXiv:2608.23120v1 Announce Type: cross Abstract: Pnar, an Austroasiatic language spoken by approximately 0.4 million people in the Jaintia Hills of Meghalaya, lacks the digital corpora and natural l...
arXiv:2610.22542v1 Announce Type: cross Abstract: While LLMs demonstrate strong capabilities in English Relation Extraction (RE) tasks, a significant gap remains in assessing their RE performance in...
arXiv:2609.17539v1 Announce Type: new Abstract: We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The so...
arXiv:2606.13218v2 Announce Type: replace Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and m...
arXiv:2011.03783v3 Announce Type: replace-cross Abstract: In this work, we introduce the construction of a machine translation (MT) assisted and human-in-the-loop multilingual parallel corpus with an...
Pearl-Chat is an 8.5‑million‑parameter causal decoder‑only transformer built from first principles in JAX and Flax NNX, specifically engineered for the low‑resource Bantu language Luganda. It uses a byte‑level BPE tokenizer trained from scratch on curated Luganda corpora, achieving a two‑fold compression improvement over larger models like LLaMA‑3 and Gemma. The model is trained in two stages—pre‑training on diverse Luganda text and instruction fine‑tuning on dialogues—reaching a validation perplexity of 1.20 and high ROUGE‑L and BLEU scores on a held‑out diagnostic benchmark, with all weights, tokenizers, and code released openly.
This study evaluates large language models (LLMs) for English‑to‑dialectal Arabic machine translation using the MADAR dataset across 16 Arabic dialects, covering both translation directions. It compares specialized Arabic models (Jais, Nile), multilingual models (Gemma, Command‑R, Mistral, Aya), and commercial APIs (GPT‑4.1) with multiple metrics—BLEU, CHRF, COMET, GEMBA—and a small manual assessment. The authors also examine LLMs’ ability to classify dialects and discuss challenges in automatic MT evaluation for Arabic dialects, noting potential training data contamination.
arXiv:2608.30092v1 Announce Type: cross Abstract: We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single...