Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
<|reference_start|>Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios: 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.<|reference_end|>
@article{joshi2024connecting,
    title={Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National
  Varieties, Creoles and Other Low-resource Scenarios},
    author={Aditya Joshi, Diptesh Kanojia, Heather Lent, Hour Kaing, Haiyue Song},
    journal={arXiv preprint arXiv:2409.12683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12683},
    primaryClass={cs.CL cs.AI}
}