Modern Translation Systems heavily rely on high-quality, large parallel datasets for state-of-the-art performance. However, such resources are largely unavailable for most of the South Asian languages. Among them, Nepali and Tamang fall into such category, with Tamang being among the least digitally resourced languages in the region. This work addresses the gap by developing NepTam20K, a 20K gold standard parallel corpus, and NepTam80K, an 80K synthetic Nepali-Tamang parallel corpus, both sentence-aligned and designed to support machine translation. The datasets were created through a pipeline involving data scraping from Nepali news and online sources, pre-processing, semantic filtering, balancing for tense and polarity (in NepTam20K dataset), expert translation into Tamang by native speakers of the language, and verification by an expert Tamang linguist. The dataset covers five domains: Agriculture, Health, Education and Technology, Culture, and General Communication. To evaluate the dataset, baseline machine translation experiments were carried out using various multilingual pre-trained models: mBART, M2M-100, NLLB-200, and a vanilla Transformer model. The fine-tuning on the NLLB-200 achieved the highest sacreBLEU scores of 40.92 (Nepali-Tamang) and 45.26 (Tamang-Nepali).
Figures & tables
Figure 1: Overall procedure of the proposed Nepali–Tamang Machine Translation resource development
Before
\nep काठमाडौं: आजको मौसम राम्रो छ (Nepali)
kathmandu: aajako mausam ramro chha
Kathmandu: Today’s weather is good
After
\nep आजको मौसम राम्रो छ। (Nepali)
aajako mausam ramro chha.
Today’s weather is good.
Before
\nep _घरमा खाना पकाउँदैछ , । (Nepali)
Table 1: Example of Text Before and After pre-processing
Tense
Pattern (Verb)
Non Past
\nep छु, छौँ, छस्, छेस्, छौ, छ, छे, छन्, छिन्, छौं, दिनँ, दैनौँ, दैनस्, दैनौ, दिनौ
Table 3: Polarity Classification based on Verb Patterns Prasain (2011)
Category
NepTam20K
NepTam80K
Train
Test
Sentence Length
Short
5,863 (39.1%)
1,954 (39.1%)
15,941 (19.9%)
Medium
6,793 (45.3%)
2,266 (45.3%)
52,122 (65.2%)
Long
1,552 (10.3%)
518 (10.4%)
10,640 (13.3%)
Very Long
823 (5.5%)
262 (5.2%)
1,396 (1.7%)
Table 4: Category-wise Data Distribution in NepTam20K and NepTam80K Dataset.
Model
Hyperparameter
Value
NepTammBART
Epochs
7
Batch size
8
Grad. accumulation
2
Learning rate
7e-5
Weight decay
0.01
NepTamM2M
Epochs
5
Table 5: Training and fine-tuning configurations for all models on NepTam20K.
Model
Parameter Size
Direction
sacreBLEU
chrF
chrF++
METEOR
COMET
NepTamM2M
418M
ne → tam
40.24
73.30
69.27
60.81
75.67
tam → ne
42.73
72.21
68.74
61.07
79.13
NepTamNLLB
600M
ne → tam
40.92
73.98
69.94
61.44
75.89
tam → ne
45.26
73.71
70.30
62.31
80.19
NepTammBART
610.9M
ne → tam
40.14
72.91
68.92
60.46
75.60
tam → ne
42.96
71.65
68.25
60.43
79.04
Table 6: Performance of translation models trained on NepTam20K (Train), evaluated on NepTam20K (Test) set for Nepali–Tamang (ne → tam) and Tamang–Nepali (tam → ne) directions
Model
Direction
sacreBLEU
chrF
chrF++
METEOR
COMET
NepTamM2M
ne → tam
40.56
73.70
69.62
60.55
75.68
tam → ne
44.03
72.83
69.34
60.50
79.28
NepTamNLLB
ne → tam
41.79
74.58
70.60
62.19
76.12
tam → ne
48.28
75.57
72.31
64.25
81.26
NepTammBART
ne → tam
41.44
74.07
70.10
61.21
75.83
tam → ne
46.68
74.59
71.17
61.96
80.70
Table 7: Performance of translation models trained on NepTam80K(Train), evaluated on NepTam20K Test set for Nepali–Tamang (ne → tam) and Tamang–Nepali (tam → ne) directions
We release Samasāmayik, a novel, meticulously curated, large-scale Hindi-Sanskrit corpus, comprising 92,196 parallel sentences. Unlike most data available in Sanskrit, which focuses on classical era text and poetry, this corpus aggregates data from diverse sources covering contemporary materials, including spoken tutorials, children's magazines, radio conversations, and instruction materials. We benchmark this new dataset by fine-tuning three complementary models - ByT5, NLLB and IndicTrans-v2, to demonstrate its utility. Our experiments demonstrate that models trained on the Samasamayik corpus achieve significant performance gains on in-domain test data, while achieving comparable performance on other widely used test sets, establishing a strong new performance baseline for contemporary Hindi-Sanskrit translation. Furthermore, a comparative analysis against existing corpora reveals minimal semantic and lexical overlap, confirming the novelty and non-redundancy of our dataset as a robust new resource for low-resource Indic language MT.
N J Karthika, Keerthana Suryanarayanan, Jahanvi Purohit +3
Indian Institute of Technology Bombay · Geakminds Technologies Private Limited · Indian Institute of Technology Roorkee
We present a study on low-resource machine translation for the Tangkhul-English (nmf-en) language pair. Tangkhul is a severely under-resourced Tibeto-Burman language spoken primarily in Manipur, India, with virtually no prior natural language processing infrastructure. We describe two systems: (1) a primary system based on ByT5-large fine-tuned on 38,336 Tangkhul-English parallel sentence pairs, and (2) a contrastive system based on mT5-small fine-tuned on the same corpus. Our primary ByT5-large system achieves a corpus BLEU score of 39.97, chrF++ of 58.07, BERTScore F1 of 0.8104, and COMET (wmt22-comet-da) of 0.7302 on a held-out test set of 3,856 sentences. We further discuss the orthographic challenges specific to Tangkhul's Latin-script diacritics, the domain bias of our training corpus (which comprises biblical text, stories, and conversational data), and avenues for future improvement through data diversification and domain adaptation.
Machine translation (MT) for Indian languages remains constrained by the limited availability of high-quality, Indic-centric parallel corpora and evaluation benchmarks. Existing multilingual resources are largely constructed from English-pivot content and often fail to capture the linguistic diversity, cultural complexity, and domain-specific characteristics of Indian languages. We present COILD, an Indic-centric parallel corpus comprising over 1.16 million human-translated and human-verified sentence pairs, covering 20 Indian language pairs across the Indo-Aryan, Dravidian, Tibeto-Burman, and Austro-Asiatic language families. The corpus is built entirely from original Indian language sources collected from licensed repositories spanning eight domains with direct real-world applicability. Furthermore, we introduce a domain-centric benchmark comprising 2,000 expert-verified sentences to enable consistent multilingual and cross-lingual evaluation across Indian language pairs. To validate the effectiveness of COILD, we fine-tune two representative multilingual neural machine translation models, IndicTrans2-Distilled and NLLB-200. Experimental results demonstrate consistent improvements across language pairs, domains, automatic evaluation metrics, and human evaluation, highlighting the effectiveness of high-quality Indic-centric supervision. COILD provides a valuable training and evaluation resource for advancing multilingual machine translation and future multilingual language models for Indian languages.