User Interest Modelling in Argumentative Dialogue Systems

Annalena Aicher, Nadine Gerstenlauer, Wolfgang Minker, Stefan Ultes


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.
Anthology ID:
2022.lrec-1.14
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
127–136
Language:
URL:
https://aclanthology.org/2022.lrec-1.14
DOI:
Bibkey:
Cite (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.
Cite (Informal):
User Interest Modelling in Argumentative Dialogue Systems (Aicher et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.14.pdf