Call centers are critical for gathering customer feedback, making them essential for business communication. Predicting the ongoing business process status accurately has become a focus in both academia and industry. However, current methods mainly analyze process sequence data from enterprise information systems, missing out on valuable data from other sources like call centers. Moreover, these methods often focus on a single task, ignoring the shared information across multiple tasks. This paper presents a novel method for business process prediction that fuses multi-modal data from both information systems and call centers. Specifically, the method combines sequence data from the enterprise information system and dialogue text data from the call center for a more enriched business process prediction. Additionally, to navigate the multi-task learning conundrum, we improve the existing MMoE algorithm and introduce a new multi-task learning architecture called Heterogeneous Multi-gate Mixture-of-experts. The experimental results over some current approaches like Transformer, CNN and LSTM show superior prediction performance compared to baseline models, demonstrating that our method can help call centers optimize their processes, improve customer service, and drive business success.

中文翻译：

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呼叫中心场景下的多模态融合业务流程预测

呼叫中心对于收集客户反馈至关重要，这使得它们对于业务沟通至关重要。准确预测正在进行的业务流程状态已成为学术界和工业界的焦点。然而，当前的方法主要分析来自企业信息系统的流程序列数据，错过了来自呼叫中心等其他来源的有价值的数据。此外，这些方法通常只关注单个任务，而忽略多个任务之间的共享信息。本文提出了一种融合来自信息系统和呼叫中心的多模式数据的业务流程预测新方法。具体来说，该方法结合了来自企业信息系统的序列数据和来自呼叫中心的对话文本数据，以进行更丰富的业务流程预测。此外，为了解决多任务学习难题，我们改进了现有的 MMoE 算法，并引入了一种新的多任务学习架构，称为异构多门混合专家。 Transformer、CNN 和 LSTM 等当前方法的实验结果表明，与基线模型相比，我们的方法可以帮助呼叫中心优化流程、改善客户服务并推动业务成功。