[Submitted on 23 Sep 2026]
Title:COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages
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Abstract: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.
Comments: 17 pages, including references and appendices
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.28826 [cs.CL]
(or arXiv:2609.28826v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.28826
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kshetrimayum Boynao Singh [view email] [v1] Wed, 23 Sep 2026 22:19:45 UTC (23,911 KB)
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