@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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="park-etal-2022-disruptive">
<titleInfo>
<title>Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kyungjin</namePart>
<namePart type="family">Park</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hyunwoo</namePart>
<namePart type="family">Sohn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wookhee</namePart>
<namePart type="family">Min</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bradford</namePart>
<namePart type="family">Mott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Krista</namePart>
<namePart type="family">Glazewski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cindy</namePart>
<namePart type="given">E</namePart>
<namePart type="family">Hmelo-Silver</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">James</namePart>
<namePart type="family">Lester</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
</titleInfo>
<name type="personal">
<namePart type="given">Oliver</namePart>
<namePart type="family">Lemon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dilek</namePart>
<namePart type="family">Hakkani-Tur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junyi</namePart>
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arash</namePart>
<namePart type="family">Ashrafzadeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="given">Hernández</namePart>
<namePart type="family">Garcia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Malihe</namePart>
<namePart type="family">Alikhani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Vandyke</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ondřej</namePart>
<namePart type="family">Dušek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Edinburgh, UK</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">park-etal-2022-disruptive</identifier>
<identifier type="doi">10.18653/v1/2022.sigdial-1.47</identifier>
<location>
<url>https://aclanthology.org/2022.sigdial-1.47/</url>
</location>
<part>
<date>2022-09</date>
<extent unit="page">
<start>490</start>
<end>499</end>
</extent>
</part>
</mods>
</modsCollection>
%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