Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
Kyungjin Park, Hyunwoo Sohn, Wookhee Min, Bradford Mott, Krista Glazewski, Cindy E. Hmelo-Silver, James Lester
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.- Anthology ID:
- 2022.sigdial-1.47
- Volume:
- Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2022
- Address:
- Edinburgh, UK
- Editors:
- Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 490–499
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2022.sigdial-1.47/
- DOI:
- 10.18653/v1/2022.sigdial-1.47
- Cite (ACL):
- Kyungjin Park, Hyunwoo Sohn, Wookhee Min, Bradford Mott, Krista Glazewski, Cindy E. Hmelo-Silver, and James Lester. 2022. Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 490–499, Edinburgh, UK. Association for Computational Linguistics.
- Cite (Informal):
- Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network (Park et al., SIGDIAL 2022)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2022.sigdial-1.47.pdf