TabuLM is a new language model pre‑trained on Kinyarwanda tabular data, extending KinyaBERT‑large with row, column, and cell‑type embeddings and a table‑structure attention bias. It introduces two pre‑training objectives—Masked Cell Recovery and Column Type Prediction—and is trained on 172 Rwandan government tables. On the TabQA‑kin benchmark, TabuLM achieves 62.0% exact match, outperforming KinyaBERT‑large and multilingual baselines by significant margins.
By Ireddi Rakshitha, Devavarapu Yashwanth, Ntakirutimana Pierre
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
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
MoganColBERT-TR is a Turkish multi‑vector retrieval model that projects token embeddings from 768 to 128 dimensions and uses MaxSim late interaction for scoring. It builds on the previously trained MoganBERT‑TR encoder, adapting it to the ColBERT objective with a single‑epoch distillation phase using cross‑encoder teacher scores. Evaluated on five Turkish BEIR datasets in a zero‑shot setting, it achieves an overall score of 37.36, outperforming larger models on most datasets.
By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay
We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2. 0 self-supervised speech encoder.
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