BIRD-QA: A BERT-based Information Retrieval Approach to Domain Specific Question Answering
Yuhao Chen, Farhana Zulkernine
Abstract
During recent years, Question Answering (QA) systems have been widely used in many industries to provide round the clock online services to consumers from all over the world. The importance of such services became more evident during the pandemic in online medical services, education, training, marketing, system support and administration. Most of the existing systems apply simple rule-based QA strategy. Human-defined rules are used to apply pattern matching for extracting information from a given data or knowledge base to generate responses to user queries. However, rule-based pattern matching techniques are not intelligent enough to understand the context of the question to always generate appropriate responses and are static. In this work, we explored different data preprocessing strategies and BERT-style pre-trained models to build an information retrieval (IR)-based Domain specific QA framework named BIRD-QA, and created a domain specific knowledge base using website data of a university department. We implemented multiple variations of extended BERT and ALBERT-base models and validated our framework on reading comprehension task using the Stanford Question Answering Dataset (SQuAD) 1.1 and 2.0 datasets. Our extended ALBERT-based model achieved 75.4% Exact Match (EM) score and 78.8% F1 score. We also present a small feasibility test of our framework for departmental QA using data from a university website.