Machine Translation (MT) for low-resource languages remains far behind that of high-resource languages, and the gap is widest in specialised domains, where parallel data is scarce or entirely absent. We present EnSiTa, a trilingual multi-domain parallel dataset and benchmark for English, Sinhala and Tamil. EnSiTa provides human post-edited training data for seven domains, plus manually translated test sets for those and one additional domain, all produced by professional translators under a multi-year, rigorously quality-controlled process. Using this dataset, we conduct an extensive study of domain-specific MT for all six language directions, fine-tuning a from-scratch Transformer, a pre-trained translation model (NLLB-600M), and decoder-only LLMs (Gemma 3 family, 1B-12B, and TranslateGemma) across training-data sizes, model scales, and in-domain, cross-domain, multilingual and multi-domain settings. To the best of our knowledge, this is the most extensive systematically documented multi-domain parallel data creation and benchmarking effort for low-resource MT. Our data and models will be publicly released.
Figures & tables
Table 1: The sources from which the parallel and monolingual data were obtained for corpus preparation
#
Domain
Dir.
Train
Dev
Test
3
en-si
1,000
406
890
en-ta
2,000
630
1,000
Health
en-si-ta
–
–
784 †
5
en-si
6,674
1,000
1,000
en-ta
3,500
511
864
si-ta
–
–
831 †
Table 2: Statistics of corpora used for experiments. Last three have been borrowed from other sources. Totals exclude trilingual (en-si-ta) subsets marked † , which are derived from or largely overlapping with the bilingual pairs listed for the same domain and split
Figure 1: Distribution of English Sentence Lengths by Domain (Logarithmic Scale)
Figure 2: Pairwise JSD values between domains, computed on English training samples
Figure 3: KL Divergence Across All Language Pair Directions by Dataset
Figure 4: Source-domain (columns) vs cross-domain (rows) results for EnSi direction (chrF++)
Figure 5: Language direction-wise results of models trained and tested with Government data (chrF++)
Figure 6: Cross-domain results of Government-trained models for EnSi direction (chrF++)
Figure 7: Strategy comparison across training data (chrF++) for EnSi
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Gemma 3
Translate
(1B/4B/12B)
Gemma (4B/12B)
LoRA rank r
32
16
LoRA α
32
16
LoRA dropout
0.0
0.05
Adapted modules
attn + MLP
attn + MLP
Learning rate
2×10−5
1×10−5
Appendix
Table 3: Training hyperparameters for LoRA fine-tuning. The Gemma 3 recipe is held fixed across the 1B/4B/12B variants for the model-scaling study; TranslateGemma uses a lighter recipe to limit erosion of its pre-existing translation tuning. The effective batch size (32) is kept constant across model sizes by trading per-device batch against gradient accumulation.
Figure 8: Guideline document given to the translators for the Wikipedia English-Sinhala task. - Page 1
Figure 9: Guideline document given to the translators for the Wikipedia English-Sinhala task. - Page 2
Figure 10: Guideline document given to the translators for the Wikipedia English-Sinhala task. - Page 3
Figure 11: Guideline document given to the translators for the Wikipedia English-Sinhala task. - Page 4
Figure 12: Guideline document given to the translators for the Wikipedia English-Sinhala task. - Page 5
Machine translation develops computational methods for converting text, speech, and visual content between languages while preserving meaning and purpose.
We present BhashaSetu, a linguistically enriched English--Marathi parallel dataset addressing persistent data limitations in low-resource neural machine translation (NMT). Marathi, spoken by over 95 million people, remains underrepresented in high-quality parallel corpora across diverse domains. Our dataset comprises 2.78 million sentence pairs from heterogeneous sources including news, politics, healthcare, literature, and culture, with stemmed and lemmatized representations to support morphology-aware analysis. We benchmark multiple state-of-the-art translation models using BLEU, spBLEU, chrF++, and TER metrics, and conduct parameter-efficient fine-tuning of NLLB-200-distilled-600M using LoRA. A key finding from our ablation: corpus-level deduplication is the single largest preprocessing contributor to downstream quality (removing it reduces performance by 1.17 BLEU and 2.21 chrF++), demonstrating that disciplined cross-source corpus hygiene is a low-cost, high-impact intervention for low-resource, morphologically rich languages. The dataset is publicly released to promote reproducible and linguistically informed low-resource NMT research.
Param Thakkar, Anushka Yadav, Michael Tiemann +3
Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, Mumbai · Tübingen AI Center, University of Tübingen, Germany
The challenge of Machine Translation for low resource languages such as Tamil is primarily caused by the restricted amount of parallel data for these languages, as well as their substantial amount of domain variation and morphological complexity. This research presents the comprehensive evaluation of the performance of several multilingual translation models on English-Tamil and Tamil-English translations across multiple datasets: NTREX, EnTamV2, WikiMatrix and PMIndia. This study evaluates supervised NMT systems, NLLB and mBART, using both the BLEU and chrF metric, and examines how these systems perform on data of different quality levels and domains. This performs an attention-based analysis to increase model interpretability by visualising the alignments of tokens in an English source text and their Tamil translations and vice-versa to provide insight into how they make translations. This study also demonstrates that using in-context prompting can provide an excellent way to perform a few-shot translation of English to Tamil and Tamil-English using a Tamil capable TamilLaMA model, and compare this to supervised approaches qualitatively. These findings show that the quality of the datasets and their alignment with the domain will greatly affect the performance of the model, that attention-based mechanisms can aid in explain ability, and that few-shot large language models can still produce structurally coherent translations of Tamil.
Sriharshaa S, Sangeetha Sivanesan, Jaya Nirmala S
SASTRA Deemed to be University · National Institute of Technology
High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current systems still offer only limited coverage: about 200 languages on the target side, and maybe a few hundreds more on the source side, supported due to cross-lingual transfer. And even these numbers have been hard to evaluate due to the lack of reliable benchmarks and metrics. We present Omnilingual Machine Translation (OMT), the first MT system supporting more than 1,600 languages. This scale is enabled by a comprehensive data strategy that integrates large public multilingual corpora with newly created datasets, including manually curated MeDLEY bitext. We explore two ways of specializing a Large Language model (LLM) for machine translation: as a decoder-only model (OMT-LLaMA) or as a module in an encoder-decoder architecture (OMT-NLLB). Notably, all our 1B to 8B parameter models match or exceed the MT performance of a 70B LLM baseline, revealing a clear specialization advantage and enabling strong translation quality in low-compute settings. Moreover, our evaluation of English-to-1,600 translations further shows that while baseline models can interpret undersupported languages, they frequently fail to generate them with meaningful fidelity; OMT-LLaMA models substantially expand the set of languages for which coherent generation is feasible. Additionally, OMT models improve in cross-lingual transfer, being close to solving the "understanding" part of the puzzle in MT for the 1,600 evaluated. Our leaderboard and main human-created evaluation datasets (BOUQuET and Met-BOUQuET) are dynamically evolving towards Omnilinguality and freely available.