Automated Interview Evaluation System Using RoBERTa Technology

This research paper delves into the realm of automated interview evaluation, employing state-of-the-art natural language processing models. The primary focus lies in comparing the effectiveness of TF-IDF, BERT, RoBERTa, Jaccard similarity, and Word2Vec models in assessing interview responses. The paper commences by providing a comprehensive definition of automated interview evaluation, highlighting the significance of efficient and unbiased candidate assessment. The proposed methodology involves the utilization of RoBERTa, a robust transformer-based model, to analyze and score interview responses. Through meticulous experimentation and evaluation, the research scrutinizes the performance of each model, examining its ability to capture contextual nuances and semantic understanding. The final results reveal the superior efficacy of RoBERTa over the other models, demonstrating its proficiency in evaluating interview responses and emphasizing its potential for enhancing automated hiring processes. This study contributes valuable insights into the evolving field of natural language processing and automated recruitment systems.