Dialectal MT

MT: Machine Translation

Latest papers

8 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Dialect Matters: A Dialect-Aware Baseline for Igala-English Machine Translation
Nov 4, 2026Etuh E., Eli. A. JiyaDialectal MT Machine Translation
Department of Artificial Intelligence, Faculty of Computing, University of Jos, Nigeria · Federal University of Applied Sciences Kachia, Kaduna State
alkhder at AlexandriaX-2026: Context-Aware English-to-Dialectal Arabic Dialogue Translation with QLoRA-Fine-Tuned NileChat-3B
Oct 15, 2026Hasan Alkhder, Maher Pasa, Maria Hanini +2Dialectal MT Machine Translation
Sakarya University, Türkiye · Altınbaş University, Türkiye · Imperial College London +1
Precision over Scale: A Polish-Silesian Benchmark and a Translation System Outperforming Open-Source and Commercial Models
Oct 1, 2026Grzegorz Kulik, Mikołaj Pokrywka, Adam Jatowt +1Low-Resource MT Dialectal MT
NASK National Research Institute, Poland · University of Innsbruck, Austria
AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation
Sep 19, 2026Abdellah El Mekki, AbdelRahim A. Elmadany, Samar M. Magdy +6Context-Aware MT Dialectal MT
The University of British Columbia · King Fahd University of Petroleum and Minerals · Lancaster University +4
Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation
Sep 3, 2026Hasan Alkhder, Mohammad Abboush, Igor Tchappi +2Interpretable ML Low-Resource MT
Unified Multi-Dialectal Neural Machine Translation for Bangla Using the Dwadash Benchmark Corpus
Aug 12, 2026Rakib Ullah, Md. Ruhul Islam, Tanbir Ahmed +1Low-Resource MT Low-Resource Language Processing
Department of Computer Science and Engineering SYLHET ENGINEERING COLLEGE, SYLHET Sylhet, Bangladesh
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation
Apr 20, 2026Samar M. Magdy, Fakhraddin Alwajih, Abdellah El Mekki +2Arabic NLP Dialectal MT
λThe University of British Columbia · ξMinia University · γCanada Research Chair in NLP and ML
Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties
Mar 26, 2026Jannis Vamvas, Ignacio Pérez Prat, Angela Heldstab +3Dialectal MT Machine Translation
University of Zurich · Lia Rumantscha

Nov 4, 2026J/K move · Enter open · S save

Dialect Matters: A Dialect-Aware Baseline for Igala-English Machine Translation

Etuh E., Eli. A. Jiya

Department of Artificial Intelligence, Faculty of Computing, University of Jos, Nigeria · Federal University of Applied Sciences Kachia, Kaduna State

Dialectal MT Machine Translation African Language Processing Low-Resource MT

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The massive permeability of intelligent systems in every sphere of human activities have greatly enhanced clarity, ease of work, and increased general global understanding which has significantly awaken the use of intelligent systems by all users. Intelligent systems greatly rely on precise dataset/corpus for the correctness of their output. Despite efforts by researchers to address Low-resource language in the Natural Language Processing (NLP), Igala still has zero parallel corpora despite efforts by researchers. We present a dialect-aware baseline for Igala-English machine translation, among first open systems and first with dialectal variation analysis. Our dataset of 1137 sentences (899 training) is first with dialect tags: Idah 459 (40.4%), Ankpa 297 (26.1%), Ibaji 160 (14.1%), Other 187 (16.4%), Ogugu 32 (2.8%). Same English yields 20-60 Igala forms. Mixed-dialect training achieves balanced BLEU 11.66 versus Idah-only model which drops 4.7 BLEU on Ibaji (13.2 on Idah, 8.5 on Ibaji), proving dialect fairness requires mixing. Fine-tuned from Helsinki-NLP/opus-mt-en-mul (78M), epoch ablation shows convergence at 8 epochs and overfitting at 15. BLEU 11.66, chrF 46.69. Analysis shows 899 sentences on log-linear trend consistent with African low-resource literature. This work provides first dialectal impact analysis for Igala and establishes baseline for future work.