Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
/ Authors
Stephen Obadinma, Faiza Khan Khattak, Shi Wang, Tania Sidhom, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, K. Bhaskar
and 16 more authors
Bencheng Wei, I. Ren, Waqar Muhammad, Er-man Li, B. Ishola, Michael Wang, Griffin Tanner, Yu-Jia Shiah, Sean X. Zhang, K. P. Apponsah, Kanishk Patel, Jaswinder Narain, D. Pandya, Xiao-Dan Zhu, Frank Rudzicz, Elham Dolatabadi
/ 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}
Journal: Conference on Empirical Methods in Natural Language Processing