Elamur Research⌕
☰
home / papers / 2026 / Automatic Analysis of Collaboration through Human Conversational Data Resources: A Review

Automatic Analysis of Collaboration through Human Conversational Data Resources: A Review

Summary

This LREC 2026 review by Yu, Boritchev and Clavel asks how human-human, task-oriented conversation data can support automatic collaboration analysis (CollA). The authors argue that task-related aspects such as task completion already have fairly mature metrics, but the interpersonal side of working together has received far less computational attention. To set boundaries, they define collaboration through four components: a common goal, shared task understanding (rules, norms, structures), positive interdependence, and joint individual commitment. Using a recursive literature search, they select only peer-reviewed corpora that carry explicit collaboration annotations or evaluations, preferring setups where no single participant can finish the task alone. The review then covers the coding schemes and questionnaires in these corpora, the salient multimodal features, and the modeling approaches applied to them. It closes with open directions. A central observation is that resources with direct collaboration labels are rare and costly to build. The paper is a practical map of corpora and annotation instruments for anyone building collaboration-aware dialogue or human-AI systems.

Key points

  • The review sorts coding schemes into two groups:
    • Individual-level, covering cooperativeness, engagement, frustration and collective orientation.
    • Dyad/group-level, covering cohesion, collaboration quality and perceived performance.
  • Individual-level examples:
    • a Grice-based cooperation scheme in the Rovereto corpus;
    • argument-move labels (new ideas, agreements, extensions, probes/challenges) in Discussion Tracker;
    • I codes in SRI;
    • engagement annotation in RoomReader, which uses head/hand position and gaze;
    • frustration levels in MULTICOLLAB.
  • Group-level instruments:
    • Q codes for triads in SRI;
    • an adapted User Engagement Scale in MISC;
    • a modified Group Environment Questionnaire in GAME-ON;
    • teamwork self-reports in GAP;
    • cohesion ratings in AMI;
    • collaboration ratings in MULTISIMO and PhotoBook.
  • The Teams corpus combines self-assessed collective orientation with team-process questionnaires and is used for entrainment research.
  • The authors note that evaluative schemes, which judge how appropriate a contribution is, can be subjective. This makes annotator agreement hard to reach.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/24ppzvsvikh2

@inproceedings{yu2026automatic,
  title = {{Automatic Analysis of Collaboration through Human Conversational Data Resources: A Review}},
  author = {Yu, Yi and Boritchev, Maria and Clavel, Chloé},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/24ppzvsvikh2},
  url = {https://doi.org/10.63317/24ppzvsvikh2}
}

References

99 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan, et al. 2016. Openface: A General-Purpose Face Recognition Library with Mobile Applications. CMU School of Computer Science, 6(2):20. find
  2. Nikoletta Bassiou, Andreas Tsiartas, Jennifer Smith, Harry Bratt, Colleen Richey, Elizabeth Shriberg, Cynthia M D’Angelo, and Nonye Alozie. 2016. Privacy-Preserving Speech Analytics for Automatic Assessment of Student Collaboration. In Interspeech, pages 888–892. find
  3. McKenzie Braley and Gabriel Murray. 2018. The Group Affect and Performance (GAP) Corpus. In Proceedings of the Group Interaction Frontiers in Technology, GIFT’18. Association for Computing Machinery. find
  4. Michael T Braun, Steve WJ Kozlowski, Tara A Brown, and Richard P DeShon. 2020. Exploring the Dynamic Team Cohesion–Performance and Coordination–Performance Relationships of Newly Formed Teams. Small Group Research, 51(5):551–580. find
  5. Albert V Carron and Lawrence R Brawley. 2000. Cohesion: Conceptual and Measurement Issues. Small group research, 31(1):89–106. find
  6. Milly Casey-Campbell and Martin L Martens. 2009. Sticking It All Together: A Critical Assessment of the Group Cohesion–Performance Literature. International Journal of Management Reviews, 11(2):223–246. find
  7. Federica Cavicchio and Massimo Poesio. 2012. The Rovereto Emotion and Cooperation Corpus: A New Resource to Investigate Cooperation and Emotions. Language Resources and Evaluation, 46(1):117–130. find
  8. Yuxin Chen, Christopher D Andrews, Cindy E Hmelo-Silver, and Cynthia D’Angelo. 2020. Coding Schemes as Lenses on Collaborative Learning. Information and Learning Sciences, 121(1-2):1–18. find
  9. Herbert H Clark. 1996. Using Language. Cambridge University Press. find
  10. Herbert H Clark and Deanna Wilkes-Gibbs. 1986. Referring as a Collaborative Process. Cognition, 22(1):1–39. find
  11. Nicola Corbellini, Jhony H Giraldo, Giovanna Varni, and Gualtiero Volpe. 2023. Few Labels are Enough! Semi-Supervised Graph Learning for Social Interaction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3060–3068. find
  12. Bethan L Davies. 1997. Empirical Examination of Cooperation, Effort and Risk in Task-Oriented Dialogue. find
  13. Annexe Thesis Digitisation Project 2016 Block 5. find
  14. Bethan L Davies. 2007. Grice’s Cooperative Principle: Meaning and Rationality. Journal of pragmatics, 39(12):2308–2331. find
  15. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… find
  16. James E Driskell, Eduardo Salas, and Sandra Hughes. 2010. Collective Orientation and Team Performance: Development of an Individual Differences Measure. Human factors, 52(2):316– 328. find
  17. Ayesha Enayet and Gita Sukthankar. 2021a. Analyzing Team Performance with Embeddings from Multiparty Dialogues. In 2021 IEEE 15th International Conference on Semantic Computing (ICSC), pages 33–39. IEEE. find
  18. Ayesha Enayet and Gita Sukthankar. 2021b. Learning a Generalizable Model of Team Conflict from Multiparty Dialogues. International Journal of Semantic Computing, 15(04):441–460. find
  19. Ayesha Enayet and Gita Sukthankar. 2023a. find
  20. A Proactive and Generalizable Conflict Prediction Model. In 2023 IEEE 17th International Conference on Semantic Computing (ICSC), pages 216–220. IEEE. find
  21. Ayesha Enayet and Gita Sukthankar. 2023b. Improving the Generalizability of Collaborative Dialogue Analysis With Multi-Feature Embeddings. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3551–3565, Dubrovnik, Croatia. Association… find
  22. Randi A Engle and Faith R Conant. 2002. Guiding Principles for Fostering Productive Disciplinary Engagement: Explaining an Emergent Argument in a Community of Learners Classroom. Cognition and instruction, 20(4):399–483. find
  23. Florian Eyben, Martin Wöllmer, and Björn Schuller. 2010. Opensmile: The Munich Versatile and Fast Open-Source Audio Feature Extractor. In Proceedings of the 18th ACM international conference on Multimedia, pages 1459–1462. find
  24. Kristian Fenech, Ádám Fodor, Sean P Bergeron, Rachid R Saboundji, Catharine Oertel, and András Lőrincz. 2022. Perceived Personality State Estimation in Dyadic and Small Group Interaction with Deep Learning Methods. arXiv preprint arXiv:2211.04979. — [arXiv:2211.04979](https://arxiv.org/abs/2211.04979) find
  25. Patricia Goldberg, Ömer Sümer, Kathleen Stürmer, Wolfgang Wagner, Richard Göllner, Peter Gerjets, Enkelejda Kasneci, and Ulrich Trautwein. 2021. Attentive or Not? Toward a Machine Learning Approach to Assessing Students’ Visible Engagement in Classroom Instruction. Educational Psychology Review, 33(… find
  26. Herbert Paul Grice. 1975. Logic and Conversation. Syntax and semantics, 3:43–58. find
  27. Barbara J Grosz and Candace L Sidner. 1986. Attention, intentions, and the structure of discourse. Computational linguistics, 12(3):175–204. find our card
  28. Shengyue Guan, Haoyi Xiong, Jindong Wang, Jiang Bian, Bin Zhu, and Jian-guang Lou. 2025. Evaluating LLM-Based Agents for Multi-Turn Conversations: A Survey. arXiv preprint arXiv:2503.22458. — [arXiv:2503.22458](https://arxiv.org/abs/2503.22458) find
  29. Ao Guo, Atsumoto Ohashi, Ryu Hirai, Yuya Chiba, Yuiko Tsunomori, and Ryuichiro Higashinaka. 2025. User Personality and Its Influence on the Performance of Pipeline and End-to-End Task-Oriented Dialogue Systems. Scientific Reports, 15(1):20745. find
  30. Janosch Haber, Tim Baumgärtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, and Raquel Fernández. 2019. The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1895–1910, Florenc… find our card
  31. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654. find our card
  32. Peter A Heeman and Graeme Hirst. 1995. Collaborating on Referring Expressions. arXiv preprint cmp-lg/9504003. find
  33. Hayley Hung and Daniel Gatica-Perez. 2010. Estimating Cohesion in Small Groups Using Audio-visual Nonverbal Behavior. IEEE Transactions on Multimedia, 12(6):563–575. find
  34. Patrícia Jaques, Adja Andrade, João Jung, Rafael Bordini, and Rosa Vicari. 2023. Using Pedagogical Agents to Support Collaborative Distance Learning. In Computer support for collaborative learning, pages 546–547. Routledge. find
  35. David W Johnson and Roger T Johnson. 2009. An Educational Psychology Success Story: Social Interdependence Theory and Cooperative Learning. Educational researcher, 38(5):365–379. find
  36. David W Johnson and Roger T Johnson. 2013. Cooperation and the Use of Technology. In Handbook of research on educational communications and technology, pages 777–803. Routledge. find
  37. Reshmashree Bangalore Kantharaju, Caroline Langlet, Mukesh Barange, Chloé Clavel, and Catherine Pelachaud. 2020. Multimodal Analysis of Cohesion in Multi-Party Interactions. In Proceedings of the twelfth language resources and evaluation conference, pages 498–507. find
  38. Reshmashree Bangalore Kantharaju and Catherine Pelachaud. 2021. Social Signals of Cohesion in Multi-Party Interactions. In Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents, pages 9–16. find
  39. Maria Koutsombogera and Carl Vogel. 2018. Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). find
  40. Wessel Kraaij, Thomas Hain, Mike Lincoln, and Wilfried Post. 2005. The AMI Meeting Corpus. find
  41. In Proc. International Conference on Methods and Techniques in Behavioral Research, pages 1–4. find
  42. Ming Li, Xiaosheng Zhuang, Lu Bai, and Weiping Ding. 2024. Multimodal Graph Learning Based on 3D Haar Semi-Tight Framelet for Student Engagement Prediction. Information Fusion, 105:102224. find
  43. Diane Litman, Susannah Paletz, Zahra Rahimi, Stefani Allegretti, and Caitlin Rice. 2016. The Teams Corpus and Entrainment in Multi-party Spoken Dialogues. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1421–1431. find
  44. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A Robustly Optimized Bert Pretraining Approach. arXiv preprint arXiv:1907.11692. find
  45. Nichola Lubold and Heather Pon-Barry. 2014. Acoustic-prosodic Entrainment and Rapport in Collaborative Learning Dialogues. In Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge, pages 5–12. find
  46. Joni Lämsä, Pablo Uribe, Abelino Jiménez, Daniela Caballero, Raija H. Hämäläinen, and R. Araya. 2021. Deep Networks for Collaboration Analytics: Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning. find
  47. Lucien Maman, Eleonora Ceccaldi, Nale Lehmann-Willenbrock, Laurence Likforman-Sulem, Mohamed Chetouani, Gualtiero Volpe, and Giovanna Varni. 2020. Game-On: A Multimodal Dataset for Cohesion and Group Analysis. IEEE Access, 8:124185–124203. find
  48. Roberto Martinez-Maldonado, Dragan Gašević, Vanessa Echeverria, Gloria Fernandez Nieto, Zachari Swiecki, and Simon Buckingham Shum. 2021. What Do You Mean by Collaboration Analytics? A Conceptual Model. Journal of Learning Analytics, 8(1):126–153. find
  49. Daniel McDuff, Paul Thomas, Mary Czerwinski, and Nick Craswell. 2017. Multimodal Analysis of Vocal Collaborative Search: A Public Corpus and Results. In Proceedings of the 19th acm international conference on multimodal interaction, pages 456–463. find
  50. Gabriel Murray and Catharine Oertel. 2018. Predicting Group Performance in Task-based Interaction. In Proceedings of the 20th ACM International Conference on Multimodal Interaction, pages 14–20. find
  51. Marjolein C Nanninga, Yanxia Zhang, Nale Lehmann-Willenbrock, Zoltán Szlávik, and Hayley Hung. 2017. Estimating Verbal Expressions of Task and Social Cohesion in Meetings by Quantifying Paralinguistic Mimicry. In Proceedings of the 19th ACM international conference on multimodal interaction, pages 2… find
  52. Heather L O’Brien and Elaine G Toms. 2010. The Development and Evaluation of a Survey to Measure User Engagement. Journal of the American Society for Information Science and Technology, 61(1):50–69. find
  53. Jennifer K Olsen, Kshitij Sharma, Nikol Rummel, and Vincent Aleven. 2020. Temporal Analysis of Multimodal Data to Predict Collaborative Learning Outcomes. British Journal of Educational Technology, 51(5):1527–1547. find
  54. Christopher Olshefski, Luca Lugini, Ravneet Singh, Diane Litman, and Amanda Godley. 2020. The Discussion Tracker Corpus of Collaborative Argumentation. Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC’20). find
  55. Susannah BF Paletz, Diane Litman, Valerie Karuzis, Kelly M Jones, and Zahra Rahimi. 2023. Speaking Similarly: Team Personality Composition and Acoustic-Prosodic Entrainment. Small Group Research, 54(6):860–898. find
  56. Michael Peechatt, Cecilia Ovesdotter Alm, and Reynold Bailey. 2024. MULTICOLLAB: A Multimodal Corpus of Dialogues for Analyzing Collaboration and Frustration in Language. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-C… find
  57. James W Pennebaker, Martha E Francis, Roger J Booth, et al. 2001. find
  58. Linguistic Inquiry and Word Count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001. find
  59. Mannes Poel, Ronald Poppe, and Anton Nijholt. 2008. Meeting Behavior Detection in Smart Environments: Nonverbal Cues that Help to Obtain Natural Interaction. In 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pages 1–6. IEEE. find
  60. Sambit Praharaj, Maren Scheffel, Hendrik Drachsler, and Marcus Specht. 2021. Literature Review on Co-located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards? IEEE Transactions on Learning Technologies, 14(3):367–385. find
  61. Elisa Prati, Valeria Villani, Fabio Grandi, Margherita Peruzzini, and Lorenzo Sabattini. 2021. Use of Interaction Design Methodologies for Human-Robot Collaboration in Industrial Scenarios. IEEE Transactions on Automation Science and Engineering, 19(4):3126–3138. find
  62. Zahra Rahimi and Diane Litman. 2018. Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 385–390, Melbourne, Australia. Association for Computational Linguistics. find
  63. Zahra Rahimi and Diane Litman. 2020. Entrainment2vec: Embedding Entrainment for Multi-Party Dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8681–8688. find
  64. Nils Randrup, Douglas Druckenmiller, and Robert O Briggs. 2016. Philosophy of Collaboration. In 2016 49th Hawaii international conference on system sciences (HICSS), pages 898– 907. IEEE. find
  65. Justine Reverdy, Sam O’Connor Russell, Louise Duquenne, Diego Garaialde, Benjamin R Cowan, and Naomi Harte. 2022. Roomreader: A Multimodal Corpus of Online Multiparty Conversational Interactions. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2517–2527. find
  66. Colleen Richey, Cynthia M D’Angelo, Nonye Alozie, Harry Bratt, and Elizabeth Shriberg. 2016. The SRI Speech-Based Collaborative Learning Corpus. In INTERSPEECH, pages 1550–1554. find
  67. Soumaya Sabry, Lucien Maman, and Giovanna Varni. 2021. An Exploratory Computational Study on the Effect of Emergent Leadership on Social and Task Cohesion. In Companion Publication of the 2021 International Conference on Multimodal Interaction, pages 263–272. find
  68. Omar Salinas, Fabian Riquelme, Roberto Muñoz, Cristian Cechinel, Roberto Martinez, and Diego Monsalves. 2021. Can Analytics of Speaking Time Serve as Indicators of Effective Team Communication and Collaboration? In Proceedings of the X Latin American Conference on Human Computer Interaction, pages 1… find
  69. Dairazalia Sanchez-Cortes, Oya Aran, and Daniel Gatica-Perez. 2011. An Audio Visual Corpus for Emergent Leader Analysis. In Workshop on multimodal corpora for machine learning: taking stock and road mapping the future, ICMI-MLMI. find
  70. Mathilde Sassier-Roublin, Julien Saunier, and Alexandre Pauchet. 2025. Toward Real-Time Cohesion Estimation in Hybrid Groups: A Multimodal Social Signal Processing Approach. In Proceedings of the 25th ACM International Conference on Intelligent Virtual Agents, pages 1–9. find
  71. Bertrand Schneider and Tonya Bryant. 2022. Using Mobile Dual Eye-Tracking to Capture Cycles of Collaboration and Cooperation in Co-located Dyads. find
  72. Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli. 2019. Wav2vec: Unsupervised Pre-training for Speech Recognition. arXiv preprint arXiv:1904.05862. — [arXiv:1904.05862](https://arxiv.org/abs/1904.05862) find
  73. Verena Schürmann, Nicki Marquardt, and Daniel Bodemer. 2024. Conceptualization and Measurement of Peer Collaboration in Higher Education: A Systematic Review. Small Group Research, 55(1):89–138. find
  74. Jamie B Severt and Armando X Estrada. 2015. On the Function and Structure of Group Cohesion. In Team cohesion: Advances in psychological theory, methods and practice, pages 3–24. Emerald Group publishing limited. find
  75. Guy L Siebold. 1999. The Evolution of the Measurement of Cohesion. Military psychology, 11(1):5–26. find
  76. Daniel Spikol, E Ruffaldi, and M Cukurova. 2017. Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning. find
  77. Jonathan Tudge and Barbara Rogoff. 2014. Peer Influences on Cognitive Development: Piagetian and Vygotskian Perspectives. In Interaction in human development, pages 17–40. Psychology Press. find
  78. Michelle Vaccaro, Abdullah Almaatouq, and Thomas Malone. 2024. When Combinations of Humans and AI are Useful: A Systematic Review and Meta-Analysis. Nature Human Behaviour, 8(12):2293–2303. find
  79. Carl Vogel, Maria Koutsombogera, and Justine Reverdy. 2023. Aspects of Dynamics in Dialogue Collaboration. Electronics, 12(10):2210. find
  80. Fabian Walocha, Lucien Maman, Mohamed Chetouani, and Giovanna Varni. 2020. Modeling Dynamics of Task and Social Cohesion from the Group Perspective Using Nonverbal Motion Capture-Based Features. In Companion publication of the 2020 international conference on multimodal interaction, pages 182–190. find
  81. Xinru Wang, Hannah Kim, Sajjadur Rahman, Kushan Mitra, and Zhengjie Miao. 2024. Human-LLM Collaborative Annotation Through Effective Verification of LLM Labels. In Proceedings of the 2024 CHI conference on human factors in computing systems, pages 1–21. find
  82. Donna J Wood and Barbara Gray. 1991. Toward a Comprehensive Theory of Collaboration. The Journal of Applied Behavioral Science, 27(2):139–162. find
  83. Xigui Yang. 2023. A Historical Review of Collaborative Learning and Cooperative Learning. TechTrends, 67(4):718–728. find
  84. Jintian Zhang, Xin Xu, Ningyu Zhang, Ruibo Liu, Bryan Hooi, and Shumin Deng. 2024. Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14544–14607, Bangk… find
  85. Yanxia Zhang, Jeffrey Olenick, Chu-Hsiang Chang, Steve WJ Kozlowski, and Hayley Hung. 2018. TeamSense: Assessing Personal Affect and Group Cohesion in Small Teams Through Dyadic Interaction and Behavior Analysis with Wearable Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiqu… find
  86. Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Yankai Chen, Chunyu Miao, Hoang Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Langzhou He, et al. 2025. A Survey on Large Language Model Based Human-Agent Systems. Authorea Preprints. 9. find
  87. Language Resource References Braley, McKenzie and Murray, Gabriel. 2018. The Group Affect and Performance (GAP) Corpus. Association for Computing Machinery, GIFT’18. find
  88. Cavicchio, Federica and Poesio, Massimo. 2012. The Rovereto Emotion and Cooperation Corpus: A New Resource to Investigate Cooperation and Emotions. Springer. find
  89. Haber, Janosch and Baumgärtner, Tim and Takmaz, Ece and Gelderloos, Lieke and Bruni, Elia and Fernández, Raquel. 2019. The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue. Association for Computational Linguistics. find our card
  90. Kantharaju, Reshmashree B and Pelachaud, Catherine. 2021. Social Signals of Cohesion in Multi-Party Interactions. find
  91. Koutsombogera, Maria and Vogel, Carl. 2018. Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus. find
  92. Kraaij, Wessel and Hain, Thomas and Lincoln, Mike and Post, Wilfried. 2005. The AMI Meeting Corpus. find
  93. Litman, Diane and Paletz, Susannah and Rahimi, Zahra and Allegretti, Stefani and Rice, Caitlin. 2016. The Teams Corpus and Entrainment in Multi-party Spoken Dialogues. find
  94. Maman, Lucien and Ceccaldi, Eleonora and Lehmann-Willenbrock, Nale and Likforman-Sulem, Laurence and Chetouani, Mohamed and Volpe, Gualtiero and Varni, Giovanna. 2020. Game-on: A Multimodal Dataset for Cohesion and Group Analysis. IEEE. find
  95. McDuff, Daniel and Thomas, Paul and Czerwinski, Mary and Craswell, Nick. 2017. Multimodal Analysis of Vocal Collaborative Search: A Public Corpus and Results. find
  96. Olshefski, Christopher and Lugini, Luca and Singh, Ravneet and Litman, Diane and Godley, Amanda. 2020. The Discussion Tracker Corpus of Collaborative Argumentation. find
  97. Peechatt, Michael and Alm, Cecilia Ovesdotter and Bailey, Reynold. 2024. MULTICOLLAB: A Multimodal Corpus of Dialogues for Analyzing Collaboration and Frustration in Language. find
  98. Reverdy, Justine and Russell, Sam O’Connor and Duquenne, Louise and Garaialde, Diego and Cowan, Benjamin R and Harte, Naomi. 2022. Roomreader: A Multimodal Corpus of Online Multiparty Conversational Interactions. find
  99. Richey, Colleen and D’Angelo, Cynthia M and Alozie, Nonye and Bratt, Harry and Shriberg, Elizabeth. 2016. The SRI Speech-Based Collaborative Learning Corpus. ISLRN 199-041-455-836-2. find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/yu-2026-automatic-analysis-collaboration-through-human/