Dense-to-Question and Sparse-to-Answer: Hybrid Retriever System for Industrial Frequently Asked Questions

Jaehyung Seo, Taemin Lee, Hyeonseok Moon, Chanjun Park, Sugyeong Eo, Imatitikua D. Aiyanyo, Kinam Park, Aram So, Sungmin Ahn and Jeongbae Park
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Jaehyung Seo: Department of Computer Science and Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Taemin Lee: Human Inspired Artificial Intelligence Research (HIAI), Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Hyeonseok Moon: Department of Computer Science and Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Chanjun Park: Department of Computer Science and Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Sugyeong Eo: Department of Computer Science and Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Imatitikua D. Aiyanyo: Human Inspired Artificial Intelligence Research (HIAI), Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Kinam Park: Human Inspired Artificial Intelligence Research (HIAI), Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Aram So: Human Inspired Artificial Intelligence Research (HIAI), Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Sungmin Ahn: O2O Inc., 47, Maeheon-ro 8-gil, Seocho-gu, Seoul 06770, Korea
Jeongbae Park: Human Inspired Artificial Intelligence Research (HIAI), Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Korea

Mathematics, 2022, vol. 10, issue 8, 1-12

Abstract: The term “Frequently asked questions” (FAQ) refers to a query that is asked repeatedly and produces a manually constructed response. It is one of the most important factors influencing customer repurchase and brand loyalty; thus, most industry domains invest heavily in it. This has led to deep-learning-based retrieval models being studied. However, training a model and creating a database specializing in each industry domain comes at a high cost, especially when using a chatbot-based conversation system, as a large amount of resources must be continuously input for the FAQ system’s maintenance. It is also difficult for small- and medium-sized companies and national institutions to build individualized training data and databases and obtain satisfactory results. As a result, based on the deep learning information retrieval module, we propose a method of returning responses to customer inquiries using only data that can be easily obtained from companies. We hybridize dense embedding and sparse embedding in this work to make it more robust in professional terms, and we propose new functions to adjust the weight ratio and scale the results returned by the two modules.

Keywords: deep learning; artificial intelligence; natural language processing; frequently asked questions; dense and sparse embedding; industrial system; information retrieval (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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