论文标题
TADAA：实时票务分配深度学习自动顾问，以提供客户支持，帮助台和发行票务系统
TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems
论文作者
论文摘要
本文提出了TADAA：门票分配深度学习自动顾问，该顾问利用最新的变形金刚模型和机器学习技术迅速分配组织内的问题，例如客户支持，服务台和类似的发行票务系统。该项目为1）为正确组分配问题，2）将问题分配给最佳解析器，3）为解析器提供最相关的先前解决的票证。我们利用一个票务系统样本数据集，超过3K+组和超过10K+的分辨器，以获得组建议的95.2％的前3个精度，而在分辨率建议中，获得了79.0％的前5名。我们希望这项研究能够大大提高客户支持，帮助台和发行票务系统的平均问题解决时间。
This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.
