학술논문
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Document Type
Working Paper
Author
Obadinma, Stephen; Khattak, Faiza Khan; Wang, Shirley; Sidhom, Tania; Lau, Elaine; Robertson, Sean; Niu, Jingcheng; Au, Winnie; Munim, Alif; Bhaskar, Karthik Raja K.; Wei, Bencheng; Ren, Iris; Muhammad, Waqar; Li, Erin; Ishola, Bukola; Wang, Michael; Tanner, Griffin; Shiah, Yu-Jia; Zhang, Sean X.; Apponsah, Kwesi P.; Patel, Kanishk; Narain, Jaswinder; Pandya, Deval; Zhu, Xiaodan; Rudzicz, Frank; Dolatabadi, Elham
Source
Subject
Language
Abstract
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}
Comment: Camera Ready Version of Paper Published in EMNLP 2022 Industry Track
Comment: Camera Ready Version of Paper Published in EMNLP 2022 Industry Track