@inproceedings{karkada-etal-2022-strategy,
title = "Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task",
author = {Karkada, Deepthi and
Manuvinakurike, Ramesh and
Paetzel-Pr{\"u}smann, Maike and
Georgila, Kallirroi},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.620",
pages = "5768--5777",
abstract = "In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a {``}strategy-level{''} without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people{'}s descriptive strategy to ease the system{'}s language understanding in creative tasks.",
}
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%0 Conference Proceedings
%T Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task
%A Karkada, Deepthi
%A Manuvinakurike, Ramesh
%A Paetzel-Prüsmann, Maike
%A Georgila, Kallirroi
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F karkada-etal-2022-strategy
%X In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a “strategy-level” without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people’s descriptive strategy to ease the system’s language understanding in creative tasks.
%U https://aclanthology.org/2022.lrec-1.620
%P 5768-5777
Markdown (Informal)
[Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task](https://aclanthology.org/2022.lrec-1.620) (Karkada et al., LREC 2022)
ACL