@inproceedings{sandbank-etal-2018-detecting,
title = "Detecting Egregious Conversations between Customers and Virtual Agents",
author = "Sandbank, Tommy and
Shmueli-Scheuer, Michal and
Herzig, Jonathan and
Konopnicki, David and
Richards, John and
Piorkowski, David",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1163",
doi = "10.18653/v1/N18-1163",
pages = "1802--1811",
abstract = "Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20{\%} over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.",
}
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<abstract>Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.</abstract>
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%0 Conference Proceedings
%T Detecting Egregious Conversations between Customers and Virtual Agents
%A Sandbank, Tommy
%A Shmueli-Scheuer, Michal
%A Herzig, Jonathan
%A Konopnicki, David
%A Richards, John
%A Piorkowski, David
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F sandbank-etal-2018-detecting
%X Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.
%R 10.18653/v1/N18-1163
%U https://aclanthology.org/N18-1163
%U https://doi.org/10.18653/v1/N18-1163
%P 1802-1811
Markdown (Informal)
[Detecting Egregious Conversations between Customers and Virtual Agents](https://aclanthology.org/N18-1163) (Sandbank et al., NAACL 2018)
ACL
- Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, and David Piorkowski. 2018. Detecting Egregious Conversations between Customers and Virtual Agents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1802–1811, New Orleans, Louisiana. Association for Computational Linguistics.