CAMAL: A Novel Dataset for Multi-label Conversational Argument Move Analysis (2024.lrec-main)
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| Challenge: | Existing models that combine CNN and LSTM structures with speaker ID graphs improve the F1-score of our baseline models to detect speakers’ intents by a large margin. |
| Approach: | They propose a conversational multi-label corpus of teaching transcripts for Conversational Argument Move AnaLysis (CAMAL) the dataset includes 165 discussion transcripts facilitated by pre-service teachers and students . |
| Outcome: | The proposed model improves the F1-score of the baseline model to detect speakers’ intents by a large margin. |
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| Challenge: | Currently, classroom recordings are limited due to practical and privacy concerns and sharing is restricted due to limited access to valuable resources and data sets. |
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| Challenge: | Discussion Tracker provides teachers with data about argument moves, specificity and collaboration . |
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Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse (2025.coling-main)
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Jie Cao, Abhijit Suresh, Jennifer Jacobs, Charis Clevenger, Amanda Howard, Chelsea Brown, Brent Milne, Tom Fischaber, Tamara Sumner, James H. Martin
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The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)
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| Challenge: | The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio . |
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| Challenge: | In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. |
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
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In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)
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Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
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