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Artificial intelligence (AI) is rapidly transforming global communication, learning, and access to services, yet its benefits remain concentrated in high-resource languages, leaving most low-resource languages (LRLs) digitally marginalised. This chapter examines the linguistic digital divide, where large language models overwhelmingly prioritise ...
Nanlir Sallau Mullah +1 more
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Nanlir Sallau Mullah +1 more
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LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings
Conference of the European Chapter of the Association for Computational LinguisticsThe recent breakthroughs in Artificial Intelligence (AI) can be attributed to the remarkable performance of Large Language Models (LLMs) across a spectrum of research areas (e.g., machine translation, question-answering, automatic speech recognition ...
Firoj Alam +3 more
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Character Profiling in Low-Resource Language Documents
Proceedings of the 24th Australasian Document Computing Symposium, 2019This paper focuses on automatic character profiling --- connecting "who", "what" and "when" --- in literary documents. This task is especially challenging for low-resource languages, since off-the-shelf tools for named entity recognition, syntactic parsing and other natural language processing tasks are rarely available.
Tak-Sum Wong, John Lee 0001
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MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
Transactions of the Association for Computational LinguisticsInstruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their ...
Abdullatif Köksal +5 more
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Automatic Language Detection for Low Resource Ho Language
2023 OITS International Conference on Information Technology (OCIT), 2023Dula Bankira +3 more
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Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
SIGULMachine Translation has made impressive progress in recent years offering close to human-level performance on many languages, but studies have primarily focused on high-resource languages with broad online presence and resources. With the help of growing
Wan-Hua Her, U. Kruschwitz
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A Concise Survey of OCR for Low-Resource Languages
AMERICASNLPModern natural language processing (NLP) techniques increasingly require substantial amounts of data to train robust algorithms. Building such technologies for low-resource languages requires focusing on data creation efforts and data-efficient ...
Milind Agarwal, Antonios Anastasopoulos
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Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
arXiv.orgIn recent years, automatic speech recognition (ASR) systems have significantly improved, especially in languages with a vast amount of transcribed speech data.
Ara Yeroyan, Nikolay Karpov
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Fine Tuning LLMs for Low Resource Languages
2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)Large Language Models (LLMs) hold immense potential, but their data hunger can limit its performance in processing languages with limited resources. This research study explores the techniques for fine-tuning LLMs specifically for low-resource settings ...
Shreyas Joshi +5 more
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Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech Recognition
InterspeechThis paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. We introduce a novel application of weighted cross-entropy, typically used for unbalanced datasets, to facilitate the ...
Andrés Piñeiro Martín +4 more
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