Context-Based Question Answering System with Suggested Questions
Vijay Kumari, Srishti Keshari, Yashvardhan Sharma, Lavika Goel
Abstract
Question Answering and Question Generation are well-researched problems in the field of Natural Language Processing and Information Retrieval. This paper aims to demonstrate the use of novel transformer-based models like BERT, AIBERT, and DistilBERT for Question Answering System and the t5 model for Question Generation. The Question Generation task is integrated with the Question Answering System to suggest relevant questions from the input context using the transfer learning-based model. The question generation model generates questions from the context input by the user and uses different models like DistilBERT, RoBERTa for getting answers from the context. Suggested questions are ranked using BM25 scores to show the most relevant question-answer pairs on the top. The input context can be given as PDF or image(extract texts from image).