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
NE‑BERT is a multilingual encoder trained on about 8.3 million sentences from nine Northeast Indian languages plus Hindi and English. Using weighted sampling and a custom SentencePiece tokenizer, it achieves significantly lower perplexity than IndicBERT‑V2, MuRIL, and mBERT, and improves tokenization fertility. The model also addresses vocabulary fragmentation in extremely low‑resource languages through aggressive upsampling, and its effectiveness is validated on part‑of‑speech tagging for three of the languages.
By Badal Nyalang
KinyaEmbed is the first sentence‑embedding model specifically designed for Kinyarwanda, built on KinyaBERT‑large and trained through a four‑stage curriculum that incorporates paraphrase pairs, translated MNLI triplets, OPUS‑100 translation pairs, and high‑quality KinyaCOMET pairs. It outperforms existing multilingual embeddings on the SemRel2024‑rw benchmark, achieving a Spearman ψ of 0.7298, and introduces the Wiki‑RW‑STS benchmark of 300 contamination‑free Kinyarwanda sentence pairs. All model checkpoints, filtered pairs, and the new benchmark are publicly released.
By Ireddi Rakshitha, Devavarapu Yashwanth, Ntakirutimana Pierre
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.
By Haotao Xie
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 compares generative and encoder-based neural models for multilingual Named Entity Recognition (NER) across the eleven languages of the Naamapadam benchmark. Five classic model families, four decoder-only large language models fine‑tuned with LoRA and 4‑bit NF4 quantisation, and nine generative models in zero‑to‑5‑shot inference were evaluated under strict CoNLL span‑level metrics. Encoder-based models (mBERT and XLM‑R) achieved substantially higher F1 scores—up to 0.675 on Hindi—than any generative architecture, with gaps of 7.5–40 percentage points; the best few‑shot result reached only 28% of the encoder baseline. The study identifies three language clusters (encoder‑dominant, partial‑coverage, and failure‑zone) and offers deployment guidelines based on transfer learning and low‑resource NLP principles.
By Jakkala Mahesh, Jatavath Shravan Kumar, Komalla Shivani, Sujoy Sarkar