Empowering Nepali Language Models for NLP

Language models are like brainy robots that help computers understand and use human languages. For a long time, fancy models called transformers have been the star of this show, but the Nepali language, spoken by about 32 million people, has been left out of the fun. This is mainly because there’s not enough Nepali text available for training these robots. The few attempts that have been made mostly focus on simpler models, but there’s been a big gap when it comes to fancier decoder models.

To fill this gap, a bunch of smart folks collected 27.5 GB of Nepali text. That's a lot! In fact, it's 2.4 times bigger than anything else that existed for the Nepali language until now. With this mountain of data, they trained three models: BERT, RoBERTa, and GPT-2. These models have done better than the best existing models on certain tests, and they also show they can generate pretty neat Nepali text.

#A Quick Look at Natural Language Processing

Natural Language Processing (NLP) is like the magic that makes computers understand what we say. It started off as a straightforward process with rules and some fancy math. The early methods, like using n-grams, helped machines get a grip on language basics. But as languages are like a puzzle with many pieces, these methods struggled to handle the more complicated parts of communication.

Then came the cool kids: Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs). These made big improvements in working with sequences of data. They were like upgrading from a bicycle to a car when it came to language tasks. But still, they had some trouble with memory and were a bit clunky.

Then, everything changed with the arrival of the self-attention mechanism, a key feature of transformer models. This made it easier for the models to pick out what was important in a sentence. Suddenly, these models could focus on all the right words, making them smarter at understanding context. With models like ELMo, BERT, and GPT taking the spotlight, we saw some amazing jumps in performances across various tasks.

#The Growth of Instruction Tuning

Recently, a new technique known as instruction tuning has become popular. This is where models learn to follow specific commands. It’s like teaching your dog to do tricks but on a much larger scale. The models get better at responding to users and adapting to different situations. While this works great for languages with plenty of resources, like English, it's still a bit of a mystery for low-resource languages like Nepali.

#Why Focus on the Nepali Language?

Nepali is used by millions, but it has unique challenges. For example, while English often follows a Subject-Verb-Object (SVO) structure, Nepali follows a Subject-Object-Verb (SOV) structure. Plus, Nepali has its own rules about nouns, adjectives, and verbs. This makes it clear that there’s a need for specialized attention when it comes to NLP for Nepali.

With the recent advances in NLP, there’s a push to develop strong language models for low-resource languages like Nepali. So, a large dataset was compiled, made up of 27.5 GB of Nepali text scraped from the top 99 Nepali news websites. This dataset represents the largest collection of Nepali text ever put together for this purpose.

#Collecting and Cleaning the Data

The digital age has made it easier to find content in Nepali. The researchers gathered text from websites to create a solid Nepali language corpus. They checked existing datasets, like the Nepali Wikipedia and OSCAR, but decided to create their own from scratch to make sure everything was fresh and unique.

To get the best data, they removed any duplicates and cleaned it up, making sure that any non-Nepali content was taken out. They also tackled the problems often seen in web-scraped data, like HTML tags and strange symbols, to make the text neat and tidy. After tidying up everything, they ended up with a polished dataset of 27.5 GB ready for training.

#Why Tokenization Matters

Tokenization is a fancy word for breaking down text into smaller pieces, like chopping up an onion to make it easier to cook. Traditional methods split text based on spaces and punctuation but had their limits. This is where Byte-Pair Encoding (BPE) came to the rescue.

BPE helps manage rare words by breaking them down into smaller, meaningful parts. It’s especially useful for languages like Nepali, where words can change a lot based on their context. For their study, the researchers used BPE to create two tokenizers, one with 30,522 words and another with 50,256 words, ensuring they could process Nepali text effectively.

#Training the Models

The researchers went ahead and trained three models: BERT, RoBERTa, and GPT-2. Both BERT and RoBERTa are based on the transformer architecture. They might look similar, but they were trained differently. BERT has two main tasks, predicting missing words and figuring out if one sentence follows another. RoBERTa, on the other hand, just focused on predicting missing words, and it turned out to perform better.

The BERT model was trained with 110 million parameters, while RoBERTa had the same, but with a different approach. After training, they found that RoBERTa was slightly better at understanding Nepali text, scoring impressively high in performance.

For GPT-2, the team trained the model to predict the next words based on what came before. This way of training means it learns how to write text that sounds natural. After a lot of hard work, GPT-2 also showed good performance in generating Nepali text.

#Evaluating the Models

Once the models were trained, it was time to test them. For BERT and RoBERTa, they used the Nep-gLUE benchmark, which has tasks to test how well the models can understand Nepali. The models were put through their paces, and guess what? They outperformed all existing models! With a score of 95.60, they showed they could understand Nepali much better than others.

For the GPT-2 model, there wasn’t a dedicated benchmark, so the team used a summarization test. They found that while the model did well, it sometimes struggled with longer texts. This was likely due to the model's training setup, which didn’t handle super-long sentences very well.

#Results Summary

The results of the evaluations showed the big leap forward for Nepali NLP. The new models set new records, proving that with the right resources and training approaches, even low-resource languages can shine. The study’s contributions were significant, setting up a solid foundation for future research and application in languages that often get overlooked.

#Gratitude

The team behind this impressive work is grateful for the support they received along the way, including access to high-tech computing resources. Without this help, creating these amazing models might have been a lot more challenging. They hope that their work inspires even more advancements in the field of language processing, especially for those languages that don’t get the spotlight they deserve.

#Conclusion

The advancement of language models for the Nepali language is a game changer. It shows that with a bit of creativity, hard work, and the right tools, low-resource languages can finally get the attention they need. With models like BERT, RoBERTa, and GPT-2 ready to help, the future looks bright for Nepali NLP. This could lead to better tools and services for Nepali speakers, making it easier to communicate and access information in their language. Just imagine a world where computers can chat in Nepali as easily as they can in English!