Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
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DOI：
10.18653/v1/2022.sigdial-1.47
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kyungjin Park;Hyunwoo Sohn;Wookhee Min;Bradford W. Mott;Krista D. Glazewski;C. Hmelo‐Silver;James Lester
中科院分区：
文献类型：
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作者：
Kyungjin Park;Hyunwoo Sohn;Wookhee Min;Bradford W. Mott;Krista D. Glazewski;C. Hmelo‐Silver;James Lester
准确检测和适当处理多方对话中的破坏性谈话对于用户实现共同目标至关重要。在协作游戏为基础的学习环境中，检测和参加破坏性谈话具有显着的潜力，因为它可以导致分心，并产生负面的学习经验的学生。我们提出了一种新的基于注意力的用户感知神经结构，用于破坏性谈话检测，该结构使用基于序列丢弃的正则化机制。破坏性谈话检测模型进行了评估与多方对话收集从72名中学生互动的协作游戏为基础的学习环境。我们提出的破坏性谈话检测模型显着优于竞争对手的基线方法，并显示出显着的潜力，帮助支持有效的协作学习经验。
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.