The paper introduces Padyam2Gadyam, a dataset of 600 13th‑17th Century Telugu poems paired with human‑verified Telugu and English prose translations. It evaluates two traditional machine translation systems and five large language models on zero‑shot poem‑to‑prose translation, finding that general‑purpose LLMs outperform the MT systems but still exhibit systematic issues in generating and evaluating prose translations.
arXiv:2606. 12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
The paper presents a corpus of 1,262 Classical Tamil verse‑commentary pairs and evaluates several neural representation learning models—including recurrent, Transformer, Siamese, mBART‑style encoder‑decoder, and decoder‑only language models—against a TF‑IDF baseline. Experiments reveal limited gains: token‑F1 scores range from 0.02 to 0.20, the encoder‑decoder continues to lower training loss even after validation loss rises, and the decoder‑only model only reproduces authentic word order in 95.5% of minimal‑pair tests but fails to generate held‑out commentary content. The authors release the extraction and evaluation protocol while noting that redistribution of the source commentaries requires permission.
arXiv:2609.23951v1 Announce Type: new Abstract: Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior w...
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:2608. 15932v1 Announce Type: new Abstract: As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality.
As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existi...
TransClean introduces a benchmark for identifying and removing translation noise—unwanted text such as language labels, explanations, or bilingual repetitions—from large language model (LLM) outputs. The authors analyzed 790,000 translations from 12 LLMs across 22 language pairs, cataloguing 12 common noise patterns and creating 9,900 noisy‑clean pairs (8,800 synthetic, 1,100 authentic). They evaluated two extraction methods—a span‑based approach using quality estimation models and an LLM‑prompted method—demonstrating the first systematic framework to assess and improve translation cleanliness.
The paper presents a pipeline that leverages large language models to extract grammatical rules, example sentences, and lexicons from descriptive grammar books, producing synthetic parallel corpora for fine‑tuning machine translation models. Evaluated on three low‑resource languages—Kalamang, Tuatschin, and Mandan—the synthetic data improves translation quality over seed‑data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, achieving up to +8.8 ChrF++ gains. A factorial study across 96 configurations identifies which combinations of target part‑of‑speech, retrieval granularity, and sample volume drive performance gains and where they fail, demonstrating that static linguistic documentation can be repurposed for practical translation tools for severely under‑resourced languages.
PARSA‑Bench is the first dedicated benchmark for evaluating large audio‑language models on Persian, addressing unique challenges such as classical poetry, traditional music, and code‑switching. It comprises 16 tasks—10 of which are new—covering speech understanding, paralinguistic analysis, and culturally grounded audio reasoning. Across most tasks, text‑only baselines outperform audio‑based models, indicating that audio understanding remains the main limitation, except for Persian poetry where prosody provides additional information that audio beats text.
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 paper introduces a new benchmark for Urdu‑to‑English idiomatic translation, featuring 4,000 manually verified sentence pairs in both native Perso‑Arabic script and Romanized Urdu. It evaluates multiple tasks—translation, paraphrasing, idiom span detection, and back‑translation—using various prompting strategies, and finds that state‑of‑the‑art large language models outperform traditional neural machine translation systems, especially in preserving figurative meaning. The study also highlights challenges posed by the lack of standardized orthography in Romanized Urdu, which affects consistency and idiom span detection.