摘要：印度语言的机器翻译（MT）仍受限于高质量、以印度语言为中心的平行语料库和评测基准不足。现有多语种资源大多基于以英语为枢纽的内容构建，往往未能捕捉印度语言的语言多样性、文化复杂性及领域特征。我们提出 COILD，这是一套以印度语言为中心的平行语料库，包含超过 116 万个人工翻译且经人工验证的句对，覆盖印欧语系、达罗毗荼语系、藏缅语系和南亚语系中的 20 个印度语言对。该语料库完全由从授权存储库收集的原始印度语言来源构建，涵盖 8 个具备直接现实应用价值的领域。此外，我们引入了一个以领域为中心的基准，其中包含 2,000 个经专家验证的句子，以支持在印度语言对之间开展一致的多语种与跨语言评测。为验证 COILD 的有效性，我们对两个具有代表性的多语种神经机器翻译模型 IndicTrans2-Distilled 和 NLLB-200 进行了微调。实验结果表明，在不同语言对、领域、自动评估指标和人工评测中均实现了稳定改进，凸显了高质量印度语中心监督数据的有效性。COILD 为推进印度语言的多语种机器翻译以及未来多语种语言模型提供了宝贵的训练和评测资源。

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.