Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios

• URL: http://arxiv.org/abs/2409.12683v1
• Date: Thu, 19 Sep 2024 11:48:42 GMT
• Title: Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
• Authors: Aditya Joshi, Diptesh Kanojia, Heather Lent, Hour Kaing, Haiyue Song,
• Abstract summary: Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in lower-resource' scenarios. This tutorial will identify common challenges, approaches, and themes in natural language processing (NLP) research for confronting and overcoming the obstacles inherent to data-poor contexts.
• Score: 11.460959151493055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in `lower-resource' scenarios such as dialects/sociolects (national or social varieties of a language), Creoles (languages arising from linguistic contact between multiple languages) and other low-resource languages. This introductory tutorial will identify common challenges, approaches, and themes in natural language processing (NLP) research for confronting and overcoming the obstacles inherent to data-poor contexts. By connecting past ideas to the present field, this tutorial aims to ignite collaboration and cross-pollination between researchers working in these scenarios. Our notion of `lower-resource' broadly denotes the outstanding lack of data required for model training - and may be applied to scenarios apart from the three covered in the tutorial.

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