Bradford Mott


2023

pdf
Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers
Vikram Kumaran | Jonathan Rowe | Bradford Mott | Snigdha Chaturvedi | James Lester
Findings of the Association for Computational Linguistics: ACL 2023

Recognizing classroom dialogue acts has significant promise for yielding insight into teaching, student learning, and classroom dynamics. However, obtaining K-12 classroom dialogue data with labels is a significant challenge, and therefore, developing data-efficient methods for classroom dialogue act recognition is essential. This work addresses the challenge of classroom dialogue act recognition from limited labeled data using a contrastive learning-based self-supervised approach (SSCon). SSCon uses two independent models that iteratively improve each other’s performance by increasing the accuracy of dialogue act recognition and minimizing the embedding distance between the same dialogue acts. We evaluate the approach on three complementary dialogue act recognition datasets: the TalkMoves dataset (annotated K-12 mathematics lesson transcripts), the DailyDialog dataset (multi-turn daily conversation dialogues), and the Dialogue State Tracking Challenge 2 (DSTC2) dataset (restaurant reservation dialogues). Results indicate that our self-supervised contrastive learning-based model outperforms competitive baseline models when trained with limited examples per dialogue act. Furthermore, SSCon outperforms other few-shot models that require considerably more labeled data.

2022

pdf
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
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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.

2015

pdf
NCSU_SAS_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization
Wookhee Min | Bradford Mott
Proceedings of the Workshop on Noisy User-generated Text

2010

pdf
Exploring Individual Differences in Student Writing with a Narrative Composition Support Environment
Julius Goth | Alok Baikadi | Eun Young Ha | Jonathan Rowe | Bradford Mott | James Lester
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids

pdf
Exploring the Effectiveness of Lexical Ontologies for Modeling Temporal Relations with Markov Logic
Eun Y. Ha | Alok Baikadi | Carlyle Licata | Bradford Mott | James Lester
Proceedings of the SIGDIAL 2010 Conference