@inproceedings{aicher-etal-2022-user,
title = "User Interest Modelling in Argumentative Dialogue Systems",
author = "Aicher, Annalena and
Gerstenlauer, Nadine and
Minker, Wolfgang and
Ultes, Stefan",
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-1.14",
pages = "127--136",
abstract = "Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9{\%} and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.",
}
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%0 Conference Proceedings
%T User Interest Modelling in Argumentative Dialogue Systems
%A Aicher, Annalena
%A Gerstenlauer, Nadine
%A Minker, Wolfgang
%A Ultes, Stefan
%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 aicher-etal-2022-user
%X Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.
%U https://aclanthology.org/2022.lrec-1.14
%P 127-136
Markdown (Informal)
[User Interest Modelling in Argumentative Dialogue Systems](https://aclanthology.org/2022.lrec-1.14) (Aicher et al., LREC 2022)
ACL
- Annalena Aicher, Nadine Gerstenlauer, Wolfgang Minker, and Stefan Ultes. 2022. User Interest Modelling in Argumentative Dialogue Systems. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 127–136, Marseille, France. European Language Resources Association.