@inproceedings{ganhotra-etal-2020-conversational,
title = "Conversational Document Prediction to Assist Customer Care Agents",
author = "Ganhotra, Jatin and
Roitman, Haggai and
Cohen, Doron and
Mills, Nathaniel and
Gunasekara, Chulaka and
Mass, Yosi and
Joshi, Sachindra and
Lastras, Luis and
Konopnicki, David",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.25/",
doi = "10.18653/v1/2020.emnlp-main.25",
pages = "349--356",
abstract = "A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents that customer care agents can use to facilitate users' needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds."
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%0 Conference Proceedings
%T Conversational Document Prediction to Assist Customer Care Agents
%A Ganhotra, Jatin
%A Roitman, Haggai
%A Cohen, Doron
%A Mills, Nathaniel
%A Gunasekara, Chulaka
%A Mass, Yosi
%A Joshi, Sachindra
%A Lastras, Luis
%A Konopnicki, David
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ganhotra-etal-2020-conversational
%X A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users’ needs. We study the task of predicting the documents that customer care agents can use to facilitate users’ needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.
%R 10.18653/v1/2020.emnlp-main.25
%U https://aclanthology.org/2020.emnlp-main.25/
%U https://doi.org/10.18653/v1/2020.emnlp-main.25
%P 349-356
Markdown (Informal)
[Conversational Document Prediction to Assist Customer Care Agents](https://aclanthology.org/2020.emnlp-main.25/) (Ganhotra et al., EMNLP 2020)
ACL
- Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, and David Konopnicki. 2020. Conversational Document Prediction to Assist Customer Care Agents. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 349–356, Online. Association for Computational Linguistics.