Few-Shot Question Answering in Low-Resource Languages using Model-Agnostic Meta-Learning (MAML)

Abstract

Question Thanks to massive datasets like SQuAD, Question Answering (QA) systems have made tremendous strides in languages with abundant resources, such as English. Unfortunately, model performance is severely constrained in low-resource languages due to the lack of annotated data. The Model-Agnostic Meta- Learning (MAML) framework is suggested in this study as a means of few-shot quality assurance in languages with limited resources. With only a small number of annotated question-answer pairs, the method allows for quick domain or language adaptation. With an emphasis on low-resource Indian languages like Telugu, Bengali, and Hindi, we assess the framework using the multilingual QA standards TyDiQA and XQuAD. Our MAML-based technique achieves an 8.5% increase in F1 score and an 8.2% improvement in Exact Match (EM) over the best fine-tuned baselines, according to experimental data. This method considerably outperforms standard fine-tuning and transfer learning approaches in few-shot circumstances. This study demonstrates how meta-learning may be used to create flexible and scalable quality assurance systems for languages that aren't widely used.