Augmentation of Local Government FAQs using Community-based Question-answering Data

Abstract

To reduce the cost of administrative services, many local governments provide a frequently asked questions (FAQ) page on their websites that lists the questions received from local inhabitants with their official responses. The number of Q&A items posted on the FAQ page, however, will vary depending on the local government. To address this issue, we propose a method for augmenting local government FAQs by using a community-based Q&A (cQA) service. We also propose a new FAQ augmentation task to identify the regional dependence of Q&A to achieve the goal mentioned above. In our experiments, we fine-tuned the bidirectional encoder representations from transformers (BERT) model for this task, using a labeled local-government FAQ dataset. We found that the regional dependence of Q&As can be identified with high accuracy by using both the question and the answer as clues and with fine tuning for the deeper layers in BERT.