AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching

Autor:	Laskar, Md Tahmid Rahman, Chen, Cheng, Fu, Xue-Yong, Azizi, Mahsa, Bhushan, Shashi, Corston-Oliver, Simon
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents. Comment: ACL 2023 Industry Track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.17619 Zobrazit plný text záznamu View this record from Arxiv