← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-industry.108 · 记录 #1
Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
摘要
Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction
Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.
查看此版本解读 →查看此版本原文 ↗