@inproceedings{peskov-etal-2020-takes,
title = "It Takes Two to Lie: One to Lie, and One to Listen",
author = "Peskov, Denis and
Cheng, Benny and
Elgohary, Ahmed and
Barrow, Joe and
Danescu-Niculescu-Mizil, Cristian and
Boyd-Graber, Jordan",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.353",
doi = "10.18653/v1/2020.acl-main.353",
pages = "3811--3854",
abstract = "Trust is implicit in many online text conversations{---}striking up new friendships, or asking for tech support. But trust can be betrayed through deception. We study the language and dynamics of deception in the negotiation-based game Diplomacy, where seven players compete for world domination by forging and breaking alliances with each other. Our study with players from the Diplomacy community gathers 17,289 messages annotated by the sender for their intended truthfulness and by the receiver for their perceived truthfulness. Unlike existing datasets, this captures deception in long-lasting relationships, where the interlocutors strategically combine truth with lies to advance objectives. A model that uses power dynamics and conversational contexts can predict when a lie occurs nearly as well as human players.",
}
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<abstract>Trust is implicit in many online text conversations—striking up new friendships, or asking for tech support. But trust can be betrayed through deception. We study the language and dynamics of deception in the negotiation-based game Diplomacy, where seven players compete for world domination by forging and breaking alliances with each other. Our study with players from the Diplomacy community gathers 17,289 messages annotated by the sender for their intended truthfulness and by the receiver for their perceived truthfulness. Unlike existing datasets, this captures deception in long-lasting relationships, where the interlocutors strategically combine truth with lies to advance objectives. A model that uses power dynamics and conversational contexts can predict when a lie occurs nearly as well as human players.</abstract>
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%0 Conference Proceedings
%T It Takes Two to Lie: One to Lie, and One to Listen
%A Peskov, Denis
%A Cheng, Benny
%A Elgohary, Ahmed
%A Barrow, Joe
%A Danescu-Niculescu-Mizil, Cristian
%A Boyd-Graber, Jordan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F peskov-etal-2020-takes
%X Trust is implicit in many online text conversations—striking up new friendships, or asking for tech support. But trust can be betrayed through deception. We study the language and dynamics of deception in the negotiation-based game Diplomacy, where seven players compete for world domination by forging and breaking alliances with each other. Our study with players from the Diplomacy community gathers 17,289 messages annotated by the sender for their intended truthfulness and by the receiver for their perceived truthfulness. Unlike existing datasets, this captures deception in long-lasting relationships, where the interlocutors strategically combine truth with lies to advance objectives. A model that uses power dynamics and conversational contexts can predict when a lie occurs nearly as well as human players.
%R 10.18653/v1/2020.acl-main.353
%U https://aclanthology.org/2020.acl-main.353
%U https://doi.org/10.18653/v1/2020.acl-main.353
%P 3811-3854
Markdown (Informal)
[It Takes Two to Lie: One to Lie, and One to Listen](https://aclanthology.org/2020.acl-main.353) (Peskov et al., ACL 2020)
ACL
- Denis Peskov, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. 2020. It Takes Two to Lie: One to Lie, and One to Listen. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3811–3854, Online. Association for Computational Linguistics.