@inproceedings{fadnis-etal-2020-agent,
title = "Agent Assist through Conversation Analysis",
author = "Fadnis, Kshitij and
Mills, Nathaniel and
Ganhotra, Jatin and
Roitman, Haggai and
Pandey, Gaurav and
Cohen, Doron and
Mass, Yosi and
Erera, Shai and
Gunasekara, Chulaka and
Contractor, Danish and
Patel, Siva and
Liao, Q. Vera and
Joshi, Sachindra and
Lastras, Luis and
Konopnicki, David",
editor = "Liu, Qun and
Schlangen, David",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-demos.20",
doi = "10.18653/v1/2020.emnlp-demos.20",
pages = "151--157",
abstract = "Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.",
}
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<abstract>Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.</abstract>
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%0 Conference Proceedings
%T Agent Assist through Conversation Analysis
%A Fadnis, Kshitij
%A Mills, Nathaniel
%A Ganhotra, Jatin
%A Roitman, Haggai
%A Pandey, Gaurav
%A Cohen, Doron
%A Mass, Yosi
%A Erera, Shai
%A Gunasekara, Chulaka
%A Contractor, Danish
%A Patel, Siva
%A Liao, Q. Vera
%A Joshi, Sachindra
%A Lastras, Luis
%A Konopnicki, David
%Y Liu, Qun
%Y Schlangen, David
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F fadnis-etal-2020-agent
%X Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.
%R 10.18653/v1/2020.emnlp-demos.20
%U https://aclanthology.org/2020.emnlp-demos.20
%U https://doi.org/10.18653/v1/2020.emnlp-demos.20
%P 151-157
Markdown (Informal)
[Agent Assist through Conversation Analysis](https://aclanthology.org/2020.emnlp-demos.20) (Fadnis et al., EMNLP 2020)
ACL
- Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, and David Konopnicki. 2020. Agent Assist through Conversation Analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 151–157, Online. Association for Computational Linguistics.