Challenge: IndicIRSuite is the first attempt at building large-scale Neural Information Retrieval resources for a large number of Indian languages.
Approach: They introduce Neural Information Retrieval resources for 11 widely spoken Indian Languages from two major Indian language families.
Outcome: Experiments show that Indic-ColBERT improves on INDIC-MARCO datasets for 11 languages, and that it can be used to improve IR for Indian languages.

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IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020.findings-emnlp)

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Challenge: In this paper, we present NLP resources for 11 major Indian languages . distributional representations are the cornerstone of modern NLP, authors say .
Approach: They introduce NLP resources for 11 major Indian languages from two major language families . monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . they also compile a benchmark for Indian language NLU to evaluate their results .
Outcome: The monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . the pre-trained language models are based on the compact ALBERT model .
IndicXNLI: Evaluating Multilingual Inference for Indian Languages (2022.emnlp-main)

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Challenge: Indic NLP has made rapid advances in terms of corpora and pre-trained models, but benchmark datasets on standard NLU tasks are limited.
Approach: They propose to use an NLI dataset for 11 Indic languages to test their accuracy.
Outcome: The proposed dataset provides useful insights into the behaviour of pre-trained models for a diverse set of languages.
A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages (2021.eacl-main)

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Challenge: We analyze multilingual transliteration for Indic languages using scripts derived from the ancient Brahmi script.
Approach: They propose a multilingual training recipe for Indic languages that utilizes orthographic similarity between English and Indic.
Outcome: The proposed training recipe improves multilingual transliteration for Indic languages.
IndicSpeech: Text-to-Speech Corpus for Indian Languages (2020.lrec-1)

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Challenge: India has 22 languages, each of them being spoken by over a million people . the current state of the art text-to-speech systems for Indian languages are lacking in the multimedia domain .
Approach: They propose to train a state-of-the-art TTS system for Hindi, Malayalam and Bengali and publish the results.
Outcome: The proposed system trains neural text-to-speech systems for Hindi, Malayalam and Bengali and makes them publicly available.
SANSKRITI: A Comprehensive Benchmark for Evaluating Language Models’ Knowledge of Indian Culture (2025.findings-acl)

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Challenge: Language models excel in syntactic and semantic analysis, while small language models struggle in region-specific contexts.
Approach: They evaluate SANSKRITI on leading Large Language Models, Indic Language Model, and Small Language Model (SLM) it covers 16 key attributes of Indian culture including rituals and ceremonies, history, tourism, cuisine, dance and music, costume, language, art, festivals, religion, medicine, transport, sports, nightlife and personalities.
Outcome: The SANSKRITI dataset covers 16 attributes of Indian culture . it reveals that many models struggle in region-specific contexts .
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)

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Challenge: Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP).
Approach: They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families.
Outcome: The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families.
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users (2023.findings-emnlp)

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Challenge: Indian subcontinent is home to diverse languages written in multiple scripts . widespread use of romanization and lack of standardization means accurate transliteration models form a critical component in the NLP stack for Indian languages used by over 735 million Internet users.
Approach: They propose to build a transliteration dataset using monolingual and parallel corpora and human annotators.
Outcome: The proposed model improves accuracy by 15% on the Dakshina test set and establishes strong baselines on the Aksharantar test set.
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages (2026.acl-long)

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Challenge: Multilingual large language models are expensive to pretrain and suffer from imbalances across languages and datasets.
Approach: They propose a family of Indian language-only autoregressive language models trained on open-source language-specific data for the five most spoken Indian languages.
Outcome: The proposed model outperforms most larger models up to 8B across all five languages.
BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)

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Challenge: Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties.
Approach: They present a unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks.
Outcome: The proposed survey covers 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks.
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages (2023.acl-long)

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Challenge: Recent studies on machine translation systems focus on high-resource languages, but focus has shifted to low-resourced languages.
Approach: They evaluate 16 metrics from a multidimensional quality metric dataset . they show pre-trained metrics have higher correlations with annotator scores .
Outcome: The proposed evaluations show that pre-trained metrics outperform COMET on Indian languages.

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