Natural language processing for low-resource languages has affected important sectors, including international development and translation. In the 1990s, the tools of natural language processing (also known as NLP) went through a change. They went from using linguistic rule-based approaches to computerized statistical-based techniques for translation services and localization services.
Almost the entire body of research on natural language processing that we have today focuses on only 20 of the 7,000 languages of the world. The rest of the languages are left understudied and thereby under-resourced. These languages are commonly referred to as “low-resource” languages. In a more global sense, low-resource languages:
- have limited presence in academia
- feature only very lightly, if at all, in language bases
- are considerably less available on technological platforms
- are less economically privileged
- are less commonly taught
- tend to represent sparse population concentrations
In times of war, public health eruptions, and social unrest, this may not seem like an issue that is particularly worthy of note. However, there are, in fact, several reasons to care about low-resource languages (also referred to as LRLs).
Low-Resource Languages in Volume
In Africa and in India, you will find around 2,000 LRLs. That huge figure is steeply compounded by the fact that those two regions, together, host 2.5 billion inhabitants, or over a fourth of the world population. This reality requires international development translation services. Through new technologies, global development campaigns can be targeted even to speakers of higher-resource languages, including through interpreting services.
Preservation of Knowledge
NLP tools have another important functionality that is helping to both prevent the extinction of certain languages. This is not only beneficial for native speakers of LRLs. This benefits those who don’t speak those languages, as well. Knowledge originally encoded in a small or disappearing language can be preserved and shared between different cultures, which can prove helpful when crafting international development campaigns with the help of translation for nonprofits.
International Development and Translation for Global Emergencies
Another important benefit of cultivating and maintaining unstable languages can be noted if we examine the context of a global emergency. The most prominent current example is the outbreak of COVID-19, which sparked a pandemic.
In the face of a major public health emergency, concepts that had previously been widely unfamiliar, such as herd immunity, became integral to containing the spread COVID-19, requiring public health translation.
Until there was a vaccine, the main tool that humanity had to slow the spread was to ensure accurate public health communication both within each country but also between countries. Our only option, at that point, was to attempt to distribute the necessary safety information to everyone person on the planet, whether they spoke a high-resource language or a low-resource language. In the case of LRLs, this was incredibly challenging, since LRLs most typically represent languages with little to no presence on the internet, and online media is the most effective way of putting out content.
International Development and Translation Challenges
There are many challenges in addressing low-resource languages. How can these issues be addressed?
It’s crucial to note that, though there may be ways to approach these linguistic deficits separately, there is one strategy that envelops the three: improving the quality of low-resource language data.
Naturally, this assumes greater implication of speakers of LRLs in studies and language bank campaigns, an endeavor often carried out by nonprofits and institutions of higher learning. These projects are thereby most normally made possible through fundraising and language research grants. At this level, it’s important to vindicate language research funding.
But there is a step further to go. After dealing with questions of funding, there are two main practices that need to be upheld to improve LRL data quality. The first is using databases and AI programs to take advantage of similarities in linguistic patterns, for instance, among the Romance languages. The second is to improve the technology that evaluates the accuracy, performance, and efficiency of such programs. These two practices will hopefully help organizations provide accurate translation for NGOs in order to help different societies communicate more effectively.
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