Augmentation of Local Government FAQs using Community-based Question-answering Data
Pages 362 - 366
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.
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- Augmentation of Local Government FAQs using Community-based Question-answering Data
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Published In
November 2020
492 pages
ISBN:9781450389228
DOI:10.1145/3428757
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- Johannes Kepler University, Linz, Austria
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 January 2021
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iiWAS '20
iiWAS '20: The 22nd International Conference on Information Integration and Web-based Applications & Services
November 30 - December 2, 2020
Chiang Mai, Thailand
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