Teacher-Student Learning on Complexity in Intelligent Routing
<|reference_start|>Teacher-Student Learning on Complexity in Intelligent Routing: Customer service is often the most time-consuming aspect for e-commerce websites, with each contact typically taking 10-15 minutes. Effectively routing customers to appropriate agents without transfers is therefore crucial for e-commerce success. To this end, we have developed a machine learning framework that predicts the complexity of customer contacts and routes them to appropriate agents accordingly. The framework consists of two parts. First, we train a teacher model to score the complexity of a contact based on the post-contact transcripts. Then, we use the teacher model as a data annotator to provide labels to train a student model that predicts the complexity based on pre-contact data only. Our experiments show that such a framework is successful and can significantly improve customer experience. We also propose a useful metric called complexity AUC that evaluates the effectiveness of customer service at a statistical level.<|reference_end|>
@article{pi2024teacher-student,
    title={Teacher-Student Learning on Complexity in Intelligent Routing},
    author={Shu-Ting Pi, Michael Yang, Yuying Zhu, Qun Liu},
    journal={arXiv preprint arXiv:2402.15665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15665},
    primaryClass={cs.LG cs.AI}
}