Overcoming Language Barriers in NLP

Natural Language Processing (NLP) is all about teaching computers how to understand human languages. It’s like trying to get your cat to understand that you want it to get off the keyboard. Some languages, however, have less available data for teaching these computer models. These languages are called Low-resource Languages (LRLs). When it comes to translating between languages, having enough examples is crucial. So, what do we do when there aren't enough examples?

#The Challenge of LRLs

Imagine trying to teach someone how to play chess but only providing them with a few pieces instead of the whole set. That’s what it feels like for NLP models dealing with LRLs. They struggle to perform tasks like Translation when they don't have enough material to learn from. This leads to the need for better methods of translation using what little data there is.

#Using Auxiliary Data

One effective way to address the lack of data is to use parallel data from related domains or languages. Think of it as sharing recipes between friends. If you have a recipe that uses potatoes, but you want to make a dish with sweet potatoes, it’s helpful to look at how your friend made their dish. In the same way, we can train translation models using examples from languages or topics that are somewhat related.

#Fine-tuning vs. Pre-training

When building translation systems, there are generally two main ways to use this auxiliary data: fine-tuning and pre-training.

• Fine-tuning is like giving your friend a few pointers on their cooking based on your experience. You already have a basic understanding, and now you just need to tweak it a little.

• Pre-training is more akin to going back to cooking school before attempting to make that sweet potato dish. It’s about starting from scratch