@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
Sidhorn, Tania and
Lau, Elaine and
Robertson, Sean and
Niu, Jingcheng and
Au, Winnie and
Munim, Alif and
Kalaiselvi Bhaskar, Karthik Raja",
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 Sidhorn, Tania
%A Lau, Elaine
%A Robertson, Sean
%A Niu, Jingcheng
%A Au, Winnie
%A Munim, Alif
%A Kalaiselvi Bhaskar, Karthik Raja
%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 Sidhorn, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, and Karthik Raja Kalaiselvi Bhaskar. 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.