Advancing Nepali Language Processing with NLUE

The Nepali language is a bit like a fine meal-it has its own unique flavors, with a complex script called Devanagari, different ways to form words, and various dialects. While this diversity is wonderful, it makes it a bit tricky when we want to use computers to understand and process Nepali text.

A benchmark called Nep-gLUE has been created to help evaluate how well models understand Nepali, but it’s not perfect. It only covers four tasks, which is like trying to judge a restaurant’s entire menu by tasting just a couple of dishes. So, to spice things up, we’ve whipped up eight new datasets, giving rise to what we call the Nepali Language Understanding Evaluation (NLUE) benchmark. This new benchmark now offers a total of twelve tasks, allowing for a much more flavorful evaluation of NLP models.

#What’s on the Menu?

The new tasks include:

• Single-sentence classification: Where models check out a single sentence and judge its meaning.
• Similarity and paraphrase tasks: Here, models see if two sentences are saying the same thing.
• Natural Language Inference (NLI) tasks: This task asks models to figure out relationships between sentences, like spotting contradictions or agreements.

By looking at how models handle these tasks, we’ve found out that many struggle with the more complex ones. It’s like trying to make a soufflé when all they know is how to whip up scrambled eggs.

#The Complexity of Nepali

Nepali is not just any language; it comes with a rich blend of nouns, adjectives, and verbs that change form based on gender, case, and number. When we throw in all the different dialects and the rich vocabulary full of homonyms, it becomes clear that getting computers to understand Nepali is a big job.

For researchers and developers, having reliable tools to evaluate how well models grasp all these unique features is essential. However, many resources are still lacking. Much like an incomplete cookbook, we need more recipes to help us create better models for Nepali.

#The Current Situation

Despite the significance of Nepali, research in computer processing and evaluation is still like a garden that needs more watering. While some foundational work has been done with the Nep-gLUE benchmark, it’s still missing critical tasks such as pronoun resolution and advanced reasoning.

That’s where our new NLUE benchmark comes in. By introducing these eight additional datasets, we’re now able to assess models more comprehensively. This means checking how they deal with tasks like:

• Sentiment Analysis (SA): Finding out whether a text is happy, sad, or neutral.
• Coreference Resolution (CR): Figuring out what a pronoun refers to in a sentence.

#Expanding Our Toolkit

The NLUE is created to build on what Nep-gLUE started. We’ve expanded the range of tasks to strengthen evaluations for Nepali language models. This expanded toolkit includes tasks that allow for better assessment of models’ abilities to tackle complex scenarios.

Creating good datasets required us to get our hands dirty. We combined automated methods and manual processes to ensure quality and relevance. We made sure the translations were accurate, and wherever suitable datasets were missing, we did the heavy lifting by creating them ourselves.

Every dataset has its own quirks and challenges, but our aim is to provide something that represents the rich diversity of Nepali.

#Testing the Models

With our new benchmark, we put several models to the test. We looked at both models trained just on Nepali and those trained on multiple languages, including Nepali. We fine-tuned them on the new tasks and evaluated their performance. It was like an Olympic trial for language models, seeing how well they could compete in various linguistic events.

We found that models generally did well on simpler tasks, like spotting nouns and verbs, but when it came to complex reasoning tasks, their performance plummeted. It’s like watching a sprinter who can zoom down the track but trips over a hurdle.

#Results and Insights

Our experiments revealed that while models perform well on basic tasks, they really struggle when it comes to more complex challenges. For example, when we tested them on tasks that required deeper understanding or reasoning, their performance dropped significantly.

This poses a critical issue: while they can recognize simple patterns, they find it hard to tackle tasks that require thoughtful understanding. The main reason for this underperformance appears to be due to limited training data, especially on tasks that require sophisticated reasoning.

#The Limitations of Current Models

Both the monolingual and multilingual models showed great skill in tasks like named entity recognition and part-of-speech tagging, but they faltered when faced with more nuanced challenges, like paraphrase detection or NLI tasks. This shows that while they are good at spotting linguistic features, they often trip over tasks that require a deeper understanding of context.

The models have been trained mainly on news data, which does not accurately reflect the full spectrum of the Nepali language. As a result, they struggle when thrown into different contexts. Imagine a chef who only knows how to cook Italian food being challenged to make a perfect sushi roll-things could get messy.

#Looking Ahead

Our new NLUE benchmark aims to fill these gaps and give researchers a solid base to build on. By providing a broader array of tasks, we hope to encourage future improvements in language models for Nepali.

The goal now is to diversify the training datasets and explore new methods to help models learn better. By creating a more representative training environment, we can support models in becoming more robust and versatile. A world of opportunities awaits as we work towards enhancing NLP research for low-resource languages like Nepali.

#Conclusion

In a world full of languages, Nepali shines brightly, but understanding it via technology still has a way to go. With the creation of the NLUE benchmark, we’re taking significant steps towards robust evaluations and advancements in natural language processing for Nepali.

Imagine how amazing it will be when we achieve a level of understanding where language models not only recognize words but also grasp the beauty and intricacies of Nepali-a true culinary feast for the mind.