BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)
Copied to clipboard
| 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. |
Similar Papers
Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in South Asia – Current Stage and Challenges (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a survey examines the current efforts and challenges of NLP models for South Asian languages . there are more than 650 languages in South Asia, but many have very limited computational resources or are missing from existing models. |
| Approach: | a survey examines efforts and challenges of NLP for South Asian languages . they focus on transformer-based models such as BERT, T5, & GPT . findings highlight substantial issues, including missing data in critical domains . |
| Outcome: | The findings highlight significant issues, including missing data in critical domains . the survey aims to raise awareness within the NLP community for more targeted data curation . |
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource (2022.lrec-1)
Copied to clipboard
| Challenge: | a new multilingual lexical database for Indian languages is proposed . the database provides words and crosslingually mapped word meanings specific to Indian languages and cultures. |
| Approach: | They propose to create a multilingual lexical database for Indian languages called IndoUKC . the database is based on existing IndoWordNet resources and is available for browsing . |
| Outcome: | The proposed database is based on the existing IndoWordNet resource and is available for download through the LiveLanguage data catalogue. |
A Multilingual Parallel Corpora Collection Effort for Indian Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Currently, neural network based approaches for machine translation are data hungry and sentence-level aligned parallel pairs are the currency. |
| Approach: | They propose to build sentence aligned parallel corpora across 10 Indian languages using online sources which have content shared across languages. |
| Outcome: | The proposed corpora significantly extends existing resources that are either not large enough or are restricted to a specific domain (such as health). |
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers (2022.lrec-1)
Copied to clipboard
| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
| Approach: | They present a survey of a multimodal dataset with different modalities according to the applications. |
| Outcome: | The proposed datasets are available online and discuss the new frontier and motivate future researches. |
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020.findings-emnlp)
Copied to clipboard
Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Avik Bhattacharyya, Mitesh M. Khapra, Pratyush Kumar
| 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 . |
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have examined the quality of labeled data in non-English languages. |
| Approach: | They annotate how datasets are created, input text and label sources, tools used to build them and what they study. |
| Outcome: | The results show that language-proficient NLP researchers' estimated availability correlates with dataset availability. |
Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages (2025.acl-long)
Copied to clipboard
Ashwin Sankar, Sparsh Jain, Nikhil Narasimhan, Devilal Choudhary, Dhairya Suman, Mohammed Safi Ur Rahman Khan, Anoop Kunchukuttan, Mitesh M Khapra, Raj Dabre
| Challenge: | Existing datasets that cover only a fraction of Indian languages lack the breadth needed to generalize beyond curated benchmarks. |
| Approach: | They propose to build the largest speech translation dataset for Indian languages . they use a three-step methodology to gather data and train a model that performs better . |
| Outcome: | The proposed model improves on existing models and is open-source with permissive licenses. |
A Survey of NLP Progress in Sino-Tibetan Low-Resource Languages (2025.naacl-long)
Copied to clipboard
| Challenge: | Despite the increasing effort in including more low-resource languages in NLP/CL development, most of the world’s languages are still absent. |
| Approach: | They propose to include low-resource languages in NLP/CL research as more resources are poured into the development of data-driven models. |
| Outcome: | The proposed language family is a low-resource language family with a small number of native speakers and government support. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
Copied to clipboard
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
Multi-layer Annotation of the Rigveda (L18-1)
Copied to clipboard
| Challenge: | Using a multi-level annotation, we present a corpus of the R. GVEDA . |
| Approach: | They propose a multi-level annotation of the R . GVEDA, a Sanskrit text composed in the 2. millenium BCE, and a basic argument identification algorithm to supplement missing verb-argument links. |
| Outcome: | The proposed model replaces verb-argument links by LSTM based model . the proposed model is based on a LS-based model to supplement missing verb-al arguments. |