Papers with Indic languages
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning (P18-3)
Copied to clipboard
| Challenge: | Indic languages are resource-scarce and do not have such parallel data due to low volume of queries. |
| Approach: | They propose a sequence-to-sequence deep learning model which trains end-to end for Indic languages, Hindi and Telugu. |
| Outcome: | The proposed model is competitive with existing spell checking and correction techniques for Indic languages. |
Generating Inflectional Errors for Grammatical Error Correction in Hindi (2020.aacl-srw)
Copied to clipboard
| Challenge: | Automated grammatical error correction is a data-heavy task . indic languages have a relatively low amount of digitized content and complex morphology . |
| Approach: | They generate a corpus of inflectional errors for training neural networks to correct grammatical errors in Hindi. |
| Outcome: | The proposed model trains on a corpus of inflectional errors extracted from Wikipedia edits. |
Efficient Continual Pre-training of LLMs for Low-resource Languages (2025.naacl-industry)
Copied to clipboard
| Challenge: | Open-source large language models (LLMs) are a promising tool for low-resource languages . however, there is still a substantial performance gap between high-resourced languages and LRLs . |
| Approach: | They develop an algorithm to select a subset of texts from a larger corpus and use it to select tokens for LLMs. |
| Outcome: | The proposed algorithm reduces the cost of continual pre-training (CPT) with large amounts of language-specific data. |
Does Transliteration Help Multilingual Language Modeling? (2023.findings-eacl)
Copied to clipboard
| Challenge: | Script diversity presents a challenge to Multilingual Language Models by reducing lexical overlap . Script diversification can be used to improve performance of MLLMs by transliterating closely related languages to a common script. |
| Approach: | They empirically measure the effect of transliteration on MLLMs by focusing on Indic languages . they find that transliterations benefit low-resource languages without negatively affecting high-resourced ones . |
| Outcome: | The proposed transliteration-based model learns sentences that are more similar across languages. |
IndicBART: A Pre-trained Model for Indic Natural Language Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. |
| Approach: | They present a multilingual sequence-to-sequence pre-trained model for Indic languages . they evaluate it on two NLG tasks: Neural Machine Translation and extreme summarization . |
| Outcome: | The proposed model performs well on low-resource translation scenarios . Script sharing, multilingual training, and better utilization contribute to the performance. |
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages (2022.emnlp-main)
Copied to clipboard
Aman Kumar, Himani Shrotriya, Prachi Sahu, Amogh Mishra, Raj Dabre, Ratish Puduppully, Anoop Kunchukuttan, Mitesh M. Khapra, Pratyush Kumar
| Challenge: | IndicNLG is a non-English language that is hampered by the scarcity of datasets. |
| Approach: | They propose to create a dataset for natural language generation for 11 Indic languages . they use a set of pre-trained models to train multilingual models . |
| Outcome: | The proposed datasets show that pre-trained models perform well in multilingual and monolingual tasks. |
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing text normalization routines that target Indic scripts are flawed when applied to multilingual automatic speech recognition models. |
| Approach: | They propose to develop text normalization routines that leverage native linguistic expertise to ensure more robust and accurate evaluations of multilingual automatic speech recognition models. |
| Outcome: | The proposed normalization routines can be leveraged to improve performance metrics for Indic languages. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
Copied to clipboard
Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |