@inproceedings{bang-etal-2021-assessing,
title = "Assessing Political Prudence of Open-domain Chatbots",
author = "Bang, Yejin and
Lee, Nayeon and
Ishii, Etsuko and
Madotto, Andrea and
Fung, Pascale",
editor = "Li, Haizhou and
Levow, Gina-Anne and
Yu, Zhou and
Gupta, Chitralekha and
Sisman, Berrak and
Cai, Siqi and
Vandyke, David and
Dethlefs, Nina and
Wu, Yan and
Li, Junyi Jessy",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigdial-1.57",
doi = "10.18653/v1/2021.sigdial-1.57",
pages = "548--555",
abstract = "Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.",
}
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<abstract>Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.</abstract>
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%0 Conference Proceedings
%T Assessing Political Prudence of Open-domain Chatbots
%A Bang, Yejin
%A Lee, Nayeon
%A Ishii, Etsuko
%A Madotto, Andrea
%A Fung, Pascale
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F bang-etal-2021-assessing
%X Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.
%R 10.18653/v1/2021.sigdial-1.57
%U https://aclanthology.org/2021.sigdial-1.57
%U https://doi.org/10.18653/v1/2021.sigdial-1.57
%P 548-555
Markdown (Informal)
[Assessing Political Prudence of Open-domain Chatbots](https://aclanthology.org/2021.sigdial-1.57) (Bang et al., SIGDIAL 2021)
ACL
- Yejin Bang, Nayeon Lee, Etsuko Ishii, Andrea Madotto, and Pascale Fung. 2021. Assessing Political Prudence of Open-domain Chatbots. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 548–555, Singapore and Online. Association for Computational Linguistics.