Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929506248294400 |
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| author | Joshi, Aditya Kanojia, Diptesh Lent, Heather Kaing, Hour Song, Haiyue |
| author_facet | Joshi, Aditya Kanojia, Diptesh Lent, Heather Kaing, Hour Song, Haiyue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios Joshi, Aditya Kanojia, Diptesh Lent, Heather Kaing, Hour Song, Haiyue Computation and Language Artificial Intelligence 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. |
| title | Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.12683 |