@inproceedings{yang-etal-2019-detecting,
title = "Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features",
author = "Yang, Wei and
Tan, Luchen and
Lu, Chunwei and
Cui, Anqi and
Li, Han and
Chen, Xi and
Xiong, Kun and
Wang, Muzi and
Li, Ming and
Pei, Jian and
Lin, Jimmy",
editor = "Loukina, Anastassia and
Morales, Michelle and
Kumar, Rohit",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-2008",
doi = "10.18653/v1/N19-2008",
pages = "56--63",
abstract = "Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority. This paper tackles the challenge of monitoring ongoing text chat dialogues to identify cases where the customer expresses such an intent, providing triage and prioritization for a separate pool of specialized agents specially trained to handle more complex situations. We describe a hybrid model that tackles this challenge by integrating recurrent neural networks with manually-engineered features. Experiments show that both components are complementary and contribute to overall recall, outperforming competitive baselines. A trial online deployment of our model demonstrates its business value in improving customer service.",
}
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<abstract>Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company’s customer service agents have the option of escalating their complaints by filing grievances with a government authority. This paper tackles the challenge of monitoring ongoing text chat dialogues to identify cases where the customer expresses such an intent, providing triage and prioritization for a separate pool of specialized agents specially trained to handle more complex situations. We describe a hybrid model that tackles this challenge by integrating recurrent neural networks with manually-engineered features. Experiments show that both components are complementary and contribute to overall recall, outperforming competitive baselines. A trial online deployment of our model demonstrates its business value in improving customer service.</abstract>
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%0 Conference Proceedings
%T Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
%A Yang, Wei
%A Tan, Luchen
%A Lu, Chunwei
%A Cui, Anqi
%A Li, Han
%A Chen, Xi
%A Xiong, Kun
%A Wang, Muzi
%A Li, Ming
%A Pei, Jian
%A Lin, Jimmy
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F yang-etal-2019-detecting
%X Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company’s customer service agents have the option of escalating their complaints by filing grievances with a government authority. This paper tackles the challenge of monitoring ongoing text chat dialogues to identify cases where the customer expresses such an intent, providing triage and prioritization for a separate pool of specialized agents specially trained to handle more complex situations. We describe a hybrid model that tackles this challenge by integrating recurrent neural networks with manually-engineered features. Experiments show that both components are complementary and contribute to overall recall, outperforming competitive baselines. A trial online deployment of our model demonstrates its business value in improving customer service.
%R 10.18653/v1/N19-2008
%U https://aclanthology.org/N19-2008
%U https://doi.org/10.18653/v1/N19-2008
%P 56-63
Markdown (Informal)
[Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features](https://aclanthology.org/N19-2008) (Yang et al., NAACL 2019)
ACL
- Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, and Jimmy Lin. 2019. Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 56–63, Minneapolis, Minnesota. Association for Computational Linguistics.