@inproceedings{laskar-etal-2023-ai,
title = "{AI} Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching",
author = "Laskar, Md Tahmid Rahman and
Chen, Cheng and
Fu, Xue-yong and
Azizi, Mahsa and
Bhushan, Shashi and
Corston-oliver, Simon",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.57",
doi = "10.18653/v1/2023.acl-industry.57",
pages = "599--607",
abstract = "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, AI can quickly determine which calls are most relevant for coaching purposes, and provide relevant feedback and insights to the contact center manager or supervisor. In this paper, we present {``}AI Coach Assis{''}, 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) queries/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 efficient and effective way to determine which calls are most relevant for coaching purposes. Extensive experimental evaluation demonstrates the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.",
}
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%0 Conference Proceedings
%T AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
%A Laskar, Md Tahmid Rahman
%A Chen, Cheng
%A Fu, Xue-yong
%A Azizi, Mahsa
%A Bhushan, Shashi
%A Corston-oliver, Simon
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F laskar-etal-2023-ai
%X 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, AI can quickly determine which calls are most relevant for coaching purposes, and provide relevant feedback and insights to the contact center manager or supervisor. In this paper, we present “AI Coach Assis”, 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) queries/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 efficient and effective way to determine which calls are most relevant for coaching purposes. Extensive experimental evaluation demonstrates the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.
%R 10.18653/v1/2023.acl-industry.57
%U https://aclanthology.org/2023.acl-industry.57
%U https://doi.org/10.18653/v1/2023.acl-industry.57
%P 599-607
Markdown (Informal)
[AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching](https://aclanthology.org/2023.acl-industry.57) (Laskar et al., ACL 2023)
ACL