Low-Resource Indic Languages in NLP

Updated 6 December 2025

Low-resource Indic languages are defined by limited digital and parallel corpora combined with rich morphological complexity.
Advanced methodologies like transfer learning, back-translation, and tailored tokenization address language-specific challenges in NLP and ASR.
Research emphasizes synthetic data generation and cross-lingual strategies to overcome data scarcity and improve performance on key benchmarks.

Low-resource Indic languages are those Indian languages that lack substantial textual, audio, or multimodal corpora for the development and evaluation of modern NLP and Speech technologies. These languages—such as Assamese, Bodo, Manipuri, Santali, Sanskrit, Maithili, Konkani, Dogri, Rajasthani, Khasi, Mizo, Bhojpuri, Magahi, and others—stand in contrast to higher-resource Indic languages like Hindi, Bengali, Tamil, or Marathi, which are widely represented in both web corpora and academic datasets. The status of low-resource extends to both the scarcity of parallel corpora, annotated benchmarks, and domain-diverse monolingual data, as well as underrepresentation in the pre-training distribution of LLMs and ASR systems. Technical approaches for adapting or evaluating models on these languages now form a major research area in cross-lingual learning, transfer, and data augmentation.

1. Corpus Scarcity, Typological Features, and Complexity

Low-resource Indic languages are distinguished by sparse monolingual and parallel textual corpora, low digital presence, and significant morphological and syntactic complexity. Corpus statistics for languages such as Bhojpuri, Magahi, and Maithili reveal type-token ratios (TTR) and morphological MATTR (moving-average TTR) exceeding those of Hindi, with Maithili exhibiting the greatest morphological diversity (word-level TTR=0.1009, MATTR=0.587, Maithili corpus) (Mundotiya et al., 2020). Orthographic syllable analyses show more complex medial-syllable distributions for Maithili, while entropy and trigram perplexity metrics also highlight rich inflectional structures that increase language complexity and compound data scarcity challenges. Phylogenetic analyses using scaled character n-gram LM scores reveal that some low-resource languages (Bhojpuri, Magahi) form tight clusters, enabling transfer and pivoting from related languages; other languages (Maithili, Santali, Khasi) are more distinct, reducing the effectiveness of direct transfer.

2. Parallel Corpora, Script Diversity, and Alignment Challenges

Parallel corpora enable supervised training of translation and cross-lingual models, yet the landscape is imbalanced: BPCC and Samanantar provide hundreds of millions of English–Indic pairs for high-resource languages, but <1 M pairs for Assamese, Odia, Santali, Maithili, Konkani, Dogri, Bodo, and others (Raja et al., 2 Mar 2025). Indic parallel corpora span Brahmic scripts (Devanagari, Bengali, Tamil, Malayalam, Gujarati), Perso-Arabic scripts (Urdu, Sindhi, Kashmiri), historical scripts (Kaithi, Tirhuta), and sometimes Latin script (Khasi, Mizo). Script variation introduces alignment errors (AER ≈10% in best curated sets, but 30–40% in web-mined corpora like CCAligned), complicates normalization, and demands cross-script tokenization. The prevalence of code-mixed and dialectal corpora further reduces the quality of translation data, necessitating sophisticated filtering, transliteration pipelines, and hybrid training set construction that combines high-quality curated data with large, filtered pseudo-parallel texts.

3. Transfer Learning, Adaptation Methodologies, and Cross-lingual Strategies

Given the near absence of direct corpora, adaptation to low-resource Indic languages relies on transfer learning from related, higher-resource languages sharing typological or script affinity. Multilingual pre-trained transformers (MuRIL, IndicBERT, mBART, M2M-100, NLLB) leverage joint subword vocabularies and cross-lingual parameter sharing. RelateLM formalizes pivoting via transliteration (mapping LRL text into RPL script, e.g., Hindi), pseudo-translation using bilingual dictionaries for synthetic pair creation, and joint MLM+alignment pretraining (Khemchandani et al., 2021). Empirical results show transliteration and language-relatedness exploited in transfer yields sizable F1 and accuracy improvements (up to 40 pp. NER gain for Oriya, further 2–5 pp. for full RelateLM over naive methods). Alignment-augmented pretraining, as in IndicRASP, pulls parallel representations together and, when paired with careful bilingual fine-tuning, yields large chrF2 and BLEU gains for truly low-resource pairs (up to +25.8 chrF2 for en→lus, WMT 24; up to +10 BLEU for Gujarati or Punjabi translation) (Sahoo et al., 4 Oct 2024).

4. Data Augmentation, Back-Translation, and Synthetic Corpora

Back-translation (BT) and masked language modeling (MLM) are critical in generating pseudo-parallel corpora for low-resource settings. Iterative BT—translating monolingual data via seed models, then retraining with synthetic data—nearly doubles usable corpus size and delivers +5–6 BLEU on en→Indic directions (SPRING Lab IITM, WMT 2024) (Sayed et al., 1 Nov 2024). MLM pretraining improves lexical coverage and, when paired with LoRA parameter-efficient fine-tuning, enables adaptation using a fraction of model capacity. Domain adaptation and related-language data mixing further boost translation scores, with paired improvements visible for languages clustered by family (Indo-Aryan or Dravidian). Data augmentation strategies must be tailored to script and domain challenges—e.g., relaxing length thresholds for Latin-script Khasi or filtering for Bengali-script Manipuri.

5. Tokenization and Morphological Preservation

Segmentation algorithms substantially affect downstream performance in low-resource Indic NER and cross-lingual tasks. Byte Pair Encoding (BPE)—standard in many multilingual models—offers compactness but is brittle in zero-shot transfer, over-merges morpheme boundaries, and fails for unseen scripts (e.g., Santali Ol Chiki, Sindhi Arabic). SentencePiece, with its unigram LM formulation and script-agnostic subword discovery, better preserves morphological boundaries, delivers higher zero-shot F1 (e.g., Assamese 88.4% vs. BPE 0; Santali 46.1% vs. BPE 12.7%), and generalizes across scripts (Pattnayak et al., 23 Apr 2025). Character-level tokenization achieves maximal coverage but incurs excessive sequence lengths, rendering it impractical for transformer-based NER. For morphologically rich and multi-script languages, models using SentencePiece with moderate vocab sizes (20–40 K) represent best practice for generalization and entity consistency.

6. Task-Specific Benchmarks: Machine Translation, QA, NER, ASR

Low-resource Indic research combines new benchmarks and adaptation recipes for key NLP and speech tasks:

MT: Combination of bilingual, related-language mixup, domain adaptation, and back-translation yields state-of-the-art translation scores; alignment-augmented pretraining and script grouping mitigate representation mismatch (Das et al., 2022, Sayed et al., 1 Nov 2024, Sahoo et al., 4 Oct 2024).
QA: Benchmarks such as IndicParam and INDIC QA BENCHMARK expose substantial performance gaps on both linguistic and factual sub-tasks (max GPT-5 avg ≈45%, zero-shot LLMs often <23.6%) (Maheshwari et al., 29 Nov 2025, Singh et al., 18 Jul 2024); instruction-tuning and few-shot prompting provide improvements but remain limited by data imbalance and English-pretraining bias.
NER: Handholding via English annotation scaffolds and bridging via continued pretraining in a related Indic language are most effective (e.g., slot-F1 Tamil improvement: +17.2 with handholding, +3.8 with bridging) (Singh et al., 25 Jun 2024).
ASR: Large-scale multilingual pretraining (Vakyansh, IndicWav2Vec) and self-supervision yield robust production-grade ASR for 18–40 Indic languages; cross-lingual representations enable transfer to unseen languages, though single-digit WERs demand ≥40 h per language (Chadha et al., 2022, Javed et al., 2021, Diwan et al., 2021).

7. Contemporary Challenges, Systemic Limitations, and Future Directions

Research reveals persistent challenges: extreme long-tail corpus imbalance, script normalization difficulties, code-mixing, dialectal variation, and domain mismatch in available datasets (Raja et al., 2 Mar 2025). Even with state-of-the-art adaptation, frontier models show performance ceilings well below those achievable for English or high-resource peers—e.g., Llama-2 pre-trained on ∼2 trillion tokens, yet Indic languages account for <0.005% (Singh et al., 25 Jun 2024). Key recommendations include synthetic corpus expansion via back-translation and inflection generators, continued pre-training on curated Indic texts, cross-lingual MLM, and architecture-level innovations (sparse MoE routing, family-aware adapters, tokenization schemes preserving sub-syllabic units). Enhanced example retrieval (PromptRefine, RELIC) exploits high-resource banks for in-context learning and reward model alignment; diversity and retrieval-augmented prompts further boost few-shot and RLHF settings (Ghosal et al., 7 Dec 2024, Ghosal et al., 19 Jun 2025). Future directions encompass multimodal resource integration, bias mitigation, code-mixed and dialectal evaluation, and expansion of annotated benchmarks and high-quality training data.

References: (Singh et al., 25 Jun 2024, Sayed et al., 1 Nov 2024, Das et al., 2022, Khemchandani et al., 2021, Raja et al., 2 Mar 2025, Sahoo et al., 4 Oct 2024, Pattnayak et al., 23 Apr 2025, Maheshwari et al., 29 Nov 2025, Singh et al., 18 Jul 2024, Chadha et al., 2022, Javed et al., 2021, Diwan et al., 2021)