Question Answering for Low Resource Languages Using Natural Language Processing
Authors:-Nirav A. Baldha
Abstract- Recent advancements in Question Answering (QA) systems have significantly improved their performance, predominantly benefiting high-resource languages. However, low-resource languages, which lack extensive linguistic resources and data, face substantial challenges in developing effective QA systems. This paper provides an in-depth review of methodologies and advancements in QA systems for low-resource languages using Natural Language Processing (NLP) techniques. We discuss various approaches, including transfer learning, multilingual models, and cross-lingual embeddings. Additionally, we highlight case studies and experimental results, aiming to offer a comprehensive overview and suggest future research directions.
