Lombard Language: A Linguistic Ghost in the Machine

Natural Language Processing (NLP) is making leaps and bounds in various languages, but let's not kid ourselves, many languages still get left behind. Lombard, an Italian language continuum, is one such example. Despite appearances, it struggles with a lack of well-curated data. The illusion of abundance from web-scraping hides the ugly truth: these datasets are riddled with errors like language misidentification and irrelevant content.

The Mirage of Abundant Data

NLP, data is king. But what happens when that data is mostly fluff? The massive datasets scraped off the web promise much but deliver little. What's worse, they're unrepresentative. They skew heavily towards Western Lombard varieties, ignoring Eastern ones almost entirely. So, what's the point of vast amounts of data if it doesn't even cover the linguistic diversity it's supposed to represent?

Seriously, ask yourself: how valuable is a dataset that’s not just incomplete, but fundamentally biased?

Orthography: The Overlooked Conflict

Let's talk orthography. When dissecting the valid portions of Lombard data, a startling discovery emerges. The orthographical systems conflict with each other across different datasets. This representational bias doesn't just affect the quality of NLP tools. It risks erasing cultural nuances and identities linked to each variety. The benchmark doesn't capture what matters most, and that’s a problem.

The paper buries the most important finding in the appendix: the need for a shift from quantity-driven scraping to community-driven curation. Community involvement can ensure not just linguistic, but cultural representation.

Why Should We Care?

If you're thinking, "Why should I care about Lombard data?" remember this is a story about power, not just performance. The implications extend beyond Lombard. Who gets to decide which languages deserve attention and resources in the AI age? Whose data? Whose labor? Whose benefit? These aren’t just theoretical questions. they've real-world consequences for equity and representation.

The real question is, will the NLP field continue to prioritize volume over substance? Or will it finally listen to the communities whose languages they're digitizing?