@inproceedings{park-etal-2022-disruptive,
title = "Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network",
author = "Park, Kyungjin and
Sohn, Hyunwoo and
Min, Wookhee and
Mott, Bradford and
Glazewski, Krista and
Hmelo-Silver, Cindy E. and
Lester, James",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.47",
doi = "10.18653/v1/2022.sigdial-1.47",
pages = "490--499",
abstract = "Accurate detection and appropriate handling of disruptive talk in multi-party dialogue is essential for users to achieve shared goals. In collaborative game-based learning environments, detecting and attending to disruptive talk holds significant potential since it can cause distraction and produce negative learning experiences for students. We present a novel attention-based user-aware neural architecture for disruptive talk detection that uses a sequence dropout-based regularization mechanism. The disruptive talk detection models are evaluated with multi-party dialogue collected from 72 middle school students who interacted with a collaborative game-based learning environment. Our proposed disruptive talk detection model significantly outperforms competitive baseline approaches and shows significant potential for helping to support effective collaborative learning experiences.",
}
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%0 Conference Proceedings
%T Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
%A Park, Kyungjin
%A Sohn, Hyunwoo
%A Min, Wookhee
%A Mott, Bradford
%A Glazewski, Krista
%A Hmelo-Silver, Cindy E.
%A Lester, James
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F park-etal-2022-disruptive
%X Accurate detection and appropriate handling of disruptive talk in multi-party dialogue is essential for users to achieve shared goals. In collaborative game-based learning environments, detecting and attending to disruptive talk holds significant potential since it can cause distraction and produce negative learning experiences for students. We present a novel attention-based user-aware neural architecture for disruptive talk detection that uses a sequence dropout-based regularization mechanism. The disruptive talk detection models are evaluated with multi-party dialogue collected from 72 middle school students who interacted with a collaborative game-based learning environment. Our proposed disruptive talk detection model significantly outperforms competitive baseline approaches and shows significant potential for helping to support effective collaborative learning experiences.
%R 10.18653/v1/2022.sigdial-1.47
%U https://aclanthology.org/2022.sigdial-1.47
%U https://doi.org/10.18653/v1/2022.sigdial-1.47
%P 490-499
Markdown (Informal)
[Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network](https://aclanthology.org/2022.sigdial-1.47) (Park et al., SIGDIAL 2022)
ACL