Parallel Corpora for Machine Translation in Low-Resource I ndic Languages: A Comprehensive Review (2025)

Abstract: Parallel corpora play an important role in training machine translation (MT) models, particularly for low-resource languages where high-quality bilingual data is scarce. This review provides a comprehensive overview of available parallel corpora for Indic languages, which span diverse linguistic families, scripts, and regional variations. We categorize these corpora into text-to-text, code-switched, and various categories of multimodal datasets, highlighting their significance in the development of robust multilingual MT systems. Beyond resource enumeration, we critically examine the challenges faced in corpus creation, including linguistic diversity, script variation, data scarcity, and the prevalence of informal textual content. We also discuss and evaluate these corpora in various terms such as alignment quality and domain representativeness. Furthermore, we address open challenges such as data imbalance across Indic languages, the trade-off between quality and quantity, and the impact of noisy, informal, and dialectal data on MT performance. Finally, we outline future directions, including leveraging cross-lingual transfer learning, expanding multilingual datasets, and integrating multimodal resources to enhance translation quality. To the best of our knowledge, this paper presents the first comprehensive review of parallel corpora specifically tailored for low-resource Indic languages in the context of machine translation.

## introduction 1.importance of parallel corpora parallel corpora are collections of texts that contain sentence-aligned translations across two or more languages @xcite . these resources play a fundamental role in machine translation (mt), cross-lingual natural language processing (nlp), and linguistic research. unlike monolingual corpora, parallel corpora enable direct learning of translation mappings, making them essential for training statistical and neural mt models @xcite . parallel corpora have been crucial in the development of mt models, starting from phrase-based statistical mt (smt) @xcite to modern neural mt (nmt) approaches @xcite . in smt systems, they provided the necessary data for learning phrase alignments and translation probabilities @xcite . with the rise of transformer-based nmt models @xcite , large-scale parallel corpora have become even more critical, as these models rely on extensive aligned data to learn high-quality translation representations. beyond mt, parallel corpora are used in cross-lingual nlp tasks such as multilingual word embeddings @xcite , zero-shot learning @xcite , and multilingual question-answering systems @xcite . these resources allow models to generalize across languages by leveraging shared semantic representations learned from translation pairs. for low-resource languages, parallel corpora are not just tools for mt but also serve a crucial role in language preservation and revitalization @xcite . many indic languages lack digitized linguistic resources, making them vulnerable to digital extinction. creating highquality parallel datasets ensures that these languages remain computationally accessible, enabling future educational tools, digital assistants, and automated translations @xcite . ## future directions the development of parallel corpora for indic languages has advanced significantly, yet challenges related to data scarcity, domain diversity, and alignment quality persist. future research must focus on expanding low-resource language coverage, improving domain adaptation, leveraging multimodal data, and enhancing automatic data generation techniques to build more robust mt and nlp systems for indic languages. ## conclusion this paper has provided a comprehensive overview of parallel corpora for indic languages, emphasizing their role in improving mt performance. while large-scale datasets like bpcc and samanantar exist, many languages remain underrepresented, necessitating more diverse and high-quality resources. challenges such as lexical diversity, script variations, and data scarcity require innovative approaches like crowdsourcing, domain-specific text collection, and multimodal resources. code-switching in digital communication also demands corpora that capture informal and mixed-language text. automatic data generation techniques like back-translation and parallel sentence mining help augment corpora, but ensuring data quality is critical. future research should address domain bias, improve evaluation metrics, and expand multimodal and low-resource language coverage. collaborative efforts between researchers and linguistic communities will be essential in enhancing accessibility and translation accuracy for indic languages.