Understanding the Challenges of Training NLP Models on Multilingual Data

This publication, authored by Nandhavardhan Battula, delves into the complexities and challenges of training multilingual Natural Language Processing (NLP) models. As multilingual NLP becomes increasingly crucial for global communication, this paper identifies the key obstacles that hinder effective model training, such as data scarcity, linguistic diversity, computational demands, and biases in evaluation. It examines the shortcomings of current solutions like pre-trained multilingual models and transfer learning, and highlights the disparity in performance between high-resource and low-resource languages. Additionally, the paper proposes future research directions focused on improving data collection for underrepresented languages, creating fairer evaluation benchmarks, and developing more efficient model architectures. The goal is to advance the inclusivity and scalability of multilingual NLP systems, ensuring that these technologies are more equitable and accessible to a wider range of languages and cultures.