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Information 2019, 10(2), 63;

MOLI: Smart Conversation Agent for Mobile Customer Service

No. 10, East Xibeiwang Rd., Haidian District, Beijing 100094, China
Alt-Moabit 91c, 10559 Berlin, Germany
Author to whom correspondence should be addressed.
Received: 8 January 2019 / Revised: 30 January 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
(This article belongs to the Special Issue Artificial Intelligence—Methodology, Systems, and Applications)
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Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end [email protected](Precision at top 1) of MOLI was 0.69 and the customers’ satisfaction was 0.73. View Full-Text
Keywords: question and answer; dialog systems; semantic matching question and answer; dialog systems; semantic matching

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Zhao, G.; Zhao, J.; Li, Y.; Alt, C.; Schwarzenberg, R.; Hennig, L.; Schaffer, S.; Schmeier, S.; Hu, C.; Xu, F. MOLI: Smart Conversation Agent for Mobile Customer Service. Information 2019, 10, 63.

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