arXiv:2606.06349v1 Announce Type: new Abstract: Several of the world's languages are still under-resourced in terms of Natural Language Processing (NLP) tools. This is mostly due to the lack of high-quality datasets to train, develop, and evaluate systems and models for several tasks, such as Machine Translation (MT). We conduct a manual audit of the parallel and monolingual corpora available for Lombard, an under-resourced language continuum from Italy. Our analysis reveals that the perceived abundance of web-scraped data is an illusion, with massive datasets plagued by severe language miside
The proliferation of AI models is exposing the critical need for high-quality, diverse linguistic data, especially for less-resourced languages, as the limitations of existing datasets become apparent through auditing.
This highlights a significant hurdle for achieving truly global and equitable AI, as the quality and representation of training data directly impact model performance and the potential for linguistic bias.
The understanding that freely available, web-scraped data for less-resourced languages is often insufficient for robust NLP development, necessitating more rigorous data collection and curation efforts.
- · Linguists and language specialists
- · Data curation platforms
- · Governments supporting linguistic diversity
- · Developers relying solely on passive web-scraping for data
- · AI models trained on misidentified or low-quality linguistic data
Increased focus on manual auditing and high-quality data generation for under-resourced languages in NLP.
Development of new methodologies and funding mechanisms for creating reliable linguistic datasets.
A potential shift in AI development to prioritize linguistic equity, leading to more inclusive and culturally nuanced models globally.
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