arXiv:2012.15621v3 Announce Type: replace Abstract: Korean is often referred to as a low-resource language in the research community. While this claim is partially true, it is also because the availability of resources is inadequately advertised and curated. This work curates and reviews a list of Korean corpora, first describing institution-level resource development, then further iterate through a list of current open datasets for different types of tasks. We then propose a direction on how open-source dataset construction and releases should be done for less-resourced languages to promote r
The paper highlights the increasing importance of language-specific AI resources as AI models become more ubiquitous and their application expands to a wider variety of languages.
Improving the availability and curation of language data for 'less-resourced' languages like Korean is critical for equitable AI development and preventing linguistic bias in future AI systems.
This work directly addresses the data scarcity issue for Korean by documenting existing resources and proposing strategies, which could lead to better Korean language models and applications.
- · Korean AI developers
- · Korean language speakers
- · NLP researchers
- · Countries with less-resourced languages
- · Developers relying solely on English-centric datasets
Improved Korean natural language processing models will emerge, enhancing applications and services for Korean speakers.
This methodology could be adopted by other 'less-resourced' language communities, fostering broader linguistic diversity in AI.
Enhanced linguistic data infrastructure in countries like Korea could contribute to independent AI development within a sovereign AI context, reducing reliance on foreign tech stacks.
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