MaQA: A Manual Text-Based Approach for Car-Specific Question Answering

In the past few years, intelligent virtual assistant technology has had a significant impact on our daily lives, enabling us to easily access information through simple voice commands. In this paper, we present MaQA (Manual Question Answering), an approach for solving domain-specific question answering in the automobile sector, aiming to broaden the impact of virtual assistants by integrating them into the automotive domain. The goal of MaQA is to accurately classify car-related questions into one of the 1526 FAQ categories. To tackle this task, we constructed the MaQA dataset in both Korean and English, each consisting of 45,641 questions, and propose an end-to-end neural FAQ model. Our approach achieved accuracy scores of 85.24% and 82.95% on the Korean and English datasets, respectively, with 12× and 13.4× faster inference, outperforming the BERT-based model in both accuracy and latency.