@inproceedings{obadinma-etal-2022-bringing,
title = "Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support",
author = "Obadinma, Stephen and
Khan Khattak, Faiza and
Wang, Shirley and
Sidhom, Tania and
Lau, Elaine and
Robertson, Sean and
Niu, Jingcheng and
Au, Winnie and
Munim, Alif and
K. Bhaskar, Karthik Raja and
Wei, Bencheng and
Ren, Iris and
Muhammad, Waqar and
Li, Erin and
Ishola, Bukola and
Wang, Michael and
Tanner, Griffin and
Shiah, Yu-Jia and
Zhang, Sean X. and
Apponsah, Kwesi P. and
Patel, Kanishk and
Narain, Jaswinder and
Pandya, Deval and
Zhu, Xiaodan and
Rudzicz, Frank and
Dolatabadi, Elham",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.44/",
doi = "10.18653/v1/2022.emnlp-industry.44",
pages = "440--450",
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}."
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<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 https://github.com/VectorInstitute/NAA.</abstract>
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%0 Conference Proceedings
%T Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
%A Obadinma, Stephen
%A Khan Khattak, Faiza
%A Wang, Shirley
%A Sidhom, Tania
%A Lau, Elaine
%A Robertson, Sean
%A Niu, Jingcheng
%A Au, Winnie
%A Munim, Alif
%A K. Bhaskar, Karthik Raja
%A Wei, Bencheng
%A Ren, Iris
%A Muhammad, Waqar
%A Li, Erin
%A Ishola, Bukola
%A Wang, Michael
%A Tanner, Griffin
%A Shiah, Yu-Jia
%A Zhang, Sean X.
%A Apponsah, Kwesi P.
%A Patel, Kanishk
%A Narain, Jaswinder
%A Pandya, Deval
%A Zhu, Xiaodan
%A Rudzicz, Frank
%A Dolatabadi, Elham
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F obadinma-etal-2022-bringing
%X 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 https://github.com/VectorInstitute/NAA.
%R 10.18653/v1/2022.emnlp-industry.44
%U https://aclanthology.org/2022.emnlp-industry.44/
%U https://doi.org/10.18653/v1/2022.emnlp-industry.44
%P 440-450
Markdown (Informal)
[Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support](https://aclanthology.org/2022.emnlp-industry.44/) (Obadinma et al., EMNLP 2022)
ACL
- Stephen Obadinma, Faiza Khan Khattak, Shirley Wang, Tania Sidhom, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, Karthik Raja K. Bhaskar, Bencheng Wei, Iris Ren, Waqar Muhammad, Erin Li, Bukola Ishola, Michael Wang, Griffin Tanner, Yu-Jia Shiah, Sean X. Zhang, Kwesi P. Apponsah, Kanishk Patel, Jaswinder Narain, Deval Pandya, Xiaodan Zhu, Frank Rudzicz, and Elham Dolatabadi. 2022. Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 440–450, Abu Dhabi, UAE. Association for Computational Linguistics.