Propositional Extraction from Collaborative Naturalistic Dialogues
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Published
Mar 17, 2025
Videep Venkatesha
Abhijnan Nath
Ibrahim Khebour
Avyakta Chelle
Mariah Bradford
Jingxuan Tu
Hannah VanderHoeven
Brady Bhalla
Austin Youngren
Jack Fitzgerald
James Pustejovsky
Nathaniel Blanchard
Nikhil Krishnaswamy
https://orcid.org/0009-0009-4471-9264
Abstract
In the realm of collaborative learning, extracting the beliefs shared within a group is a critical capability to navigate complex tasks. Inherent in this problem is the fact that in naturalistic collaborative discourse, the same propositional content may be expressed in radically different ways. This difficulty is exacerbated when speech overlaps and other communicative modalities are used, as would be the case in a co-situated collaborative task. In this paper, we conduct a comparative methodological analysis of extraction techniques for task-relevant propositions from natural speech dialogues in a challenging shared task setting where participants collaboratively determine the weights of five blocks using only a balance scale. We encode utterances and candidate propositions through language models and compare a cross-encoder method, adapted from coreference research, to a vector similarity baseline. Our cross-encoder approach outperforms both a cosine similarity baseline and zero-shot inference by both the GPT-4 and LLaMA 2 language models, and we establish a novel baseline on this challenging task on two collaborative task datasets---the Weights Task and DeliData---showing the generalizability of our approach. Furthermore, we explore the use of state of the art large language models for data augmentation to enhance performance, extend our examination to transcripts generated by Google's Automatic Speech Recognition system to assess the potential for automating the propositional extraction process in real-time, and introduce a framework for live propositional extraction from natural speech and multimodal signals. This study not only demonstrates the feasibility of detecting collaboration-relevant content in unstructured interactions but also lays the groundwork for employing AI to enhance collaborative problem-solving in classrooms, and other collaborative settings, such as the workforce. Our code may be found at: (https://github.com/csu-signal/PropositionExtraction).
How to Cite
Venkatesha, V., Nath, A., Khebour, I., Chelle, A., Bradford, M., Tu, J., … Krishnaswamy, N. (2025). Propositional Extraction from Collaborative Naturalistic Dialogues. Journal of Educational Data Mining, 17(1), 183–216. https://doi.org/10.5281/zenodo.15042102
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Keywords
collaborative problem solving, natural speech, propositional extraction, natural language processing, dialogue analysis
References
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Donnelly, P. J., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., Kelly, S., Nystrand, M., and D’Mello, S. K. 2016. Multi-sensor modeling of teacher instructional segments in live classrooms. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. Association for Computing Machinery, Tokyo, Japan, 177–184.
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Gijlers, H. and de Jong, T. 2009. Sharing and confronting propositions in collaborative inquiry learning. Cognition and Instruction 27, 3, 239–268.
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., and Hesse, F. W. 2018. Advancing the science of collaborative problem solving. Psychological Science in the Public Interest 19, 2, 59–92.
Grosz, B. J. and Sidner, C. L. 1986. Attention, intentions, and the structure of discourse. Computational Linguistics 12, 3, 175–204.
Held, W., Iter, D., and Jurafsky, D. 2021. Focus on what matters: Applying discourse coherence theory to cross document coreference. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 1406–1417.
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. 2015. Teaching machines to read and comprehend. Advances in Neural Information Processing Systems 28, 1693–1701.
Hofstätter, S., Lipani, A., Althammer, S., Zlabinger, M., and Hanbury, A. 2021. Mitigating the position bias of transformer models in passage re-ranking. In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, Proceedings, Part I 43. Springer, virtual event, 238–253.
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