arXiv Computation and Language

Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning

Hugging Face Trending Papers

Sep 16

Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning

UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is uncertain (ranging from 500,000 to 1,500,000). Syria...

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arXiv AI

Jul 28

Neural Machine Translation for Low-Resource Tangkhul--English

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.

By Chormi Zimik Vashai, Agniva Maiti

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arXiv AI

Aug 25

Statistical Machine Translation Systems of English-Pnar Language Pair : Some Insights of the Emperical Study

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...

By Edawanbiang Dhar Surmila Thokchom, Thoudam Doren Singh

nlp benchmarks

arXiv Machine Learning

Jul 28

BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis

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.

By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani

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arXiv Machine Learning

Aug 11

Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation

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.

By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli

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arXiv Computation and Language

Sep 1

When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates

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...

By Junhong Liang, Noor Abo Mokh, Bashar Alhafni

llms benchmarks

arXiv Computation and Language

6d ago

MudawanSn: A Gold-Standard Wolof-Arabic Parallel Corpus for Machine Translation

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...

By Mouhamed Mbaye, Thierno Diop

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arXiv AI

Aug 25

Towards a resource for multilingual lexicons: an MT assisted and human-in-the-loop multilingual parallel corpus with multi-word expression annotation

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...

By Lifeng Han, Najet Hadj Mohamed, Malak Rassem, Gareth Jones, Alan Smeaton, Goran Nenadic

nlp benchmarks

arXiv Machine Learning

Sep 11

E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets

E-CONAN introduces Arabic textual entailment and natural inference benchmarks comprising two datasets: E-CONAN-2 (2-way RTE) and E-CONAN-3 (3-way NLI). The datasets are built from automatically-translated pairs, human-validated machine translations, hand-crafted pairs from Arabic teaching books, and rumor-containing news headlines. The authors evaluated nine multilingual pretrained models and five large language models on these benchmarks, demonstrating that E-CONAN offers a more diverse and robust assessment than existing datasets like XNLI and ArNLI.

By Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi

llms nlp fine-tuning benchmarks

arXiv AI

Aug 7

MameLoshnLM: Yiddish Language Model and Evaluation Benchmark

arXiv:2608. 05850v1 Announce Type: cross Abstract: We present MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish.

By Uri Katz, Omer Goldman, Tomasz Limisiewicz, Reut Tsarfaty, Noah A. Smith

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arXiv AI

Sep 1

Arkios: An Open Bilingual English-Nepali Language Model Trained From Scratch, with a Devanagari-Aware Tokenizer

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...

By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun

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arXiv Computation and Language

Sep 7

Vectorizing Classical Tamil: Representation Learning for Verse-Commentary Pairs

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.

By Amrit Gopinath, Sangeetha Sivanesan

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