From Data to Dialogue: Building a Domain-Agnostic Conversational Question Answering System

In the realm of natural language processing (NLP), the quest for domain-specific question answering has been a focal point, emphasizing the precise extraction of factual information from designated knowledge sources in response to targeted inquiries. This project introduces a versatile, adaptable open-domain question answering system primed for seamless integration across diverse sectors, including educational institutions, e-commerce platforms, and healthcare portals. Leveraging the prowess of BERT and TAPAS, augmented with RAG (Retrieval-Augmented Generation), our system offers a holistic solution catering to varied information retrieval needs. Through fine-tuning on domain-specific datasets, both BERT and TAPAS capitalize on their respective strengths: BERT's contextual understanding and TAPAS's structured reasoning capabilities. The ensemble model dynamically selects the most suitable approach for each query, ensuring flexibility in handling diverse linguistic nuances and query types. Moreover, the system integrates language translation and voice output features to further enhance its adaptability. Evaluation on benchmark datasets reaffirms the system's efficacy in delivering precise and coherent responses, underscoring its potential as a multifaceted tool for enhancing user interaction and information retrieval across educational, ecommerce, healthcare, and other domains.