Interview Q&A System using Natural Language Processing
Primary Investigator (PI) Name
Md Abdullah Al Hafiz Khan
Department
CCSE - Computer Science
Abstract
Recent advancements in Question-Answering (QA) systems have exploited natural language processing models to create human-like machine interaction across diverse domains. This paper delves into the persistent challenges of constructing domain-specific QA systems, with a keen focus on the one application: the job interview. We introduce a refined model tailored to grasp the nuances of our interview-centric dataset. We focus on the improvement of existing models—Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) and Bidirectional Encoder Representations from Transformers (BERT)—and our objective is to tailor these architectures to the specificities of interview-based QA queries. Through rigorous evaluation metrics, we will present the merits of our optimized model, underscoring its potential in revolutionizing interview preparation methodologies.
Disciplines
Other Computer Sciences
Interview Q&A System using Natural Language Processing
Recent advancements in Question-Answering (QA) systems have exploited natural language processing models to create human-like machine interaction across diverse domains. This paper delves into the persistent challenges of constructing domain-specific QA systems, with a keen focus on the one application: the job interview. We introduce a refined model tailored to grasp the nuances of our interview-centric dataset. We focus on the improvement of existing models—Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) and Bidirectional Encoder Representations from Transformers (BERT)—and our objective is to tailor these architectures to the specificities of interview-based QA queries. Through rigorous evaluation metrics, we will present the merits of our optimized model, underscoring its potential in revolutionizing interview preparation methodologies.