@inproceedings{cercas-curry-rieser-2019-crowd,
title = "A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents",
author = "Cercas Curry, Amanda and
Rieser, Verena",
editor = "Nakamura, Satoshi and
Gasic, Milica and
Zukerman, Ingrid and
Skantze, Gabriel and
Nakano, Mikio and
Papangelis, Alexandros and
Ultes, Stefan and
Yoshino, Koichiro",
booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
month = sep,
year = "2019",
address = "Stockholm, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5942",
doi = "10.18653/v1/W19-5942",
pages = "361--366",
abstract = "How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as {``}polite refusal{''}, score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user{'}s perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.",
}
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<abstract>How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as “polite refusal”, score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user’s perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.</abstract>
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%0 Conference Proceedings
%T A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents
%A Cercas Curry, Amanda
%A Rieser, Verena
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F cercas-curry-rieser-2019-crowd
%X How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as “polite refusal”, score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user’s perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.
%R 10.18653/v1/W19-5942
%U https://aclanthology.org/W19-5942
%U https://doi.org/10.18653/v1/W19-5942
%P 361-366
Markdown (Informal)
[A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents](https://aclanthology.org/W19-5942) (Cercas Curry & Rieser, SIGDIAL 2019)
ACL