Towards Intelligent Process Support for Customer Service Desks: Extracting Problem Descriptions from Noisy and Multi-lingual Texts

摘要

Customer service is a differentiating capability for companies, but it faces significant challenges due to the growing individualization and connectivity of products, the increasing complexity of knowledge that service employees need to deal with, and steady cost pressure. Artificial intelligence (AI) can support service processes in a variety of ways, however, many projects simply propose replacing employees with chat bots. In contrast to pure automation focusing on customer self-service, we introduce three intelligent assistants that support service employees in their complex tasks: the scribe, the skill manager, and the background knowledge worker. In this paper, we discuss the technology and architecture underlying the skill manager in more detail. We present the results from an evaluation of commercial cognitive services from IBM and Microsoft on comprehensive real-world data that comprises over 80,000 tickets from a major IT service provider, where problem reports often comprise an email-based conversation in multiple languages. We demonstrate how today's commercially available cognitive services struggle to correctly analyze this data unless they use background ontological knowledge. We further discuss a pattern-and machine-learning based approach that we developed to extract problem descriptions from multi-lingual ticket texts, which is key to the successful application of Al-based services.