梳理低资源语言的NLP挑战与解决思路，助力方言、克里奥尔语等研究
Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
- 聚焦数据稀缺场景下的NLP共性问题与应对策略
- 总结方言、克里奥尔语等低资源语言处理方法
- 适合关注边缘语言、跨语言研究的研究者阅读
尽管大型语言模型在少数语言的基准测试中表现优异，但在数据匮乏的低资源场景下仍面临挑战，如方言/社会变体（一种语言的国家或社会变体）、克里奥尔语（多种语言接触产生的新语言）及其他低资源语言。本入门教程将识别自然语言处理（NLP）研究中的常见挑战、方法与核心主题，旨在应对数据贫乏环境下的固有障碍。通过连接过去思想与当前进展，本教程希望激发相关领域研究者的合作与知识交融。我们对‘低资源’的定义广泛涵盖模型训练所需数据严重不足的情况，可适用于本教程未直接覆盖但类似的数据稀缺场景。
原文摘要 · Abstract (English)
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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