cs.CLMar 10, 2026
SaveAutomatic Analysis of Collaboration Through Human Conversational Data Resources: A Review
Organizations: INRIA Paris, ALMAnaCH · LTCI, Télécom Paris, Institut Polytechnique de Paris, France
Abstract
Collaboration is a task-oriented, high-level human behavior. In most cases, conversation serves as the primary medium for information exchange and coordination, making conversational data a valuable resource for the automatic analysis of collaborative processes. In this paper, we focus on verbal aspects of collaboration and conduct a review of collaboration analysis using task-oriented conversation resources, encompassing related theories, coding schemes, tasks, and modeling approaches. We aim to address the question of how to utilize task-oriented human-human conversational data for collaboration analysis. We hope our review will serve as a practical resource and illuminate unexplored areas for future collaboration analysis.
Figures & tables
Figure 1: Two players playing the Hanoi tower game and conversing with each other to solve the puzzle. Their conversation provides many useful elements for CollA.
| Corpus, Description | Scheme/Questionnaire | Details | Reference | |
| Individual | SRI speech-based collaborative learning corpus | I Code (individual collaboration indicators) | regulative/logistical, interaction, and cognitive indicators of teamwork behavior | Richey et al. (2016) |
| MISC information-seeking conversations | adapted User Engagement Scale (UES) | scaled ratings of partner’s collaborativeness | McDuff et al. (2017) | |
| RoomReader multimodal, multiparty conversational interactions corpus | online engagement | continuous scaled engagement in groups based on collaborators’ behaviors and perceived intentions | Reverdy et al. (2022) | |
| Discussion Tracker multiparty discussions | collaborative argumentation functions | classification of arguments as: new ideas, agreements, extensions, probes/challenge | Olshefski et al. (2020) | |
| Rovereto emotion and cooperation corpus | cooperative dialogue effort | evaluation of each turn along three dimensions: knowledge sharing, non-cooperative behavior, and cooperation level (scaled) | Cavicchio and Poesio (2012) | |
| MULTICOLLAB multimodal dialogues | extreme emotion (frustration) in collaboration | participant self-assessed frustration level | Peechatt et al. (2024) |
Table 1: Applied coding schemes that capture different aspects of collaboration in conversational data.
| Corpus | Lang. | Hours | Task Type | Group Size | Audio | Video | Transc. | Sensors | Collaboration Annotation | Dataset Link | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Object | Obj. Level | Annotator | Granularity | ||||||||||
| AMI 2005 | EN | 100 | meeting | 4 | ✓ | ✓ | ✓ | ✗ | cohesion | group | external | segment | Dataset link |
| Rovereto 2012 | IT | 4.67 | game | 4 | ✓ | ✓ | ✓ | ✓ | cooperativeness | individual | external | segment | Dataset on request |
| SRI 2016 | EN | 26.6 | education | 3 | ✓ | ✗ | ✓ | ✗ | I Code, Q Code | both | external | segment | Dataset link |
| ✰ Teams 2016 | EN | 47 | game | 3-4 | ✓ | ✓ | ✓ | ✗ | participant’s collective orientation; group cohesion, satisfaction, potency | both | self | task | Dataset link |
| ❀ MISC 2017 | EN | 42 | game | 2 | ✓ | ✓ | ✓auto | ✗ | perceived collaboration (help/ understanding/communication) | individual | external | task | Dataset link |
Table 2: Overview of collaboration corpora arranged chronologically, to highlight the evolution of research focus in CollA. Comparative assessment across dimensions: language, recording size, task type, group size, multimodal data availability, and collaboration annotation (annotation object, object level, annotator type, and temporal granularity).
| Group-level Collaboration | Individual Collaborativeness | ||||
|---|---|---|---|---|---|
| Group Cohesion and Entrainment | Teamwork Process and Performance | Engagement | Collaboration Behaviors | Perceived Personality and Role | |
| Text 4.1 | pronoun usage ( Enayet and Sukthankar, 2021a ) lexical entrainment ( Rahimi and Litman, 2020 ) paralinguistic mimicry ( Nanninga et al., 2017 ) dialogue act Be-Positive ( Kantharaju et al., 2020 ) | syntactic entrainment embedding ( Enayet and Sukthankar, 2021a ) DACTs sequence embedding, sentiment embedding ( Enayet and Sukthankar, 2023a ; Enayet and Sukthankar, 2021a ) SUBTL score ( Murray and Oertel, 2018 ) lexical cohesion ( Rahimi and Litman, 2020 ) dependency parse feature ( Murray and Oertel, 2018 ) word psycholinguistic score ( Murray and Oertel, 2018 ) | word embedding ( Li et al., 2024 ) | underexplored | BERT embedding ( Fenech et al., 2022 ) |
| Audio 4.2 | intensity, frequency, shimmer, jitter ( Peechatt et al., 2024 ; Litman et al., 2016 ) turn-taking ( Sabry et al., 2021 ; Sassier–Roublin et al., 2025 ) laughter,backchannels ( Kantharaju and Pelachaud, 2021 ) | total overlapping, pause time ( Hung and Gatica-Perez, 2010 ) | audio embedding ( Li et al., 2024 ) | underexplored | total speaking time, pitch, jitter, loudness ( Peechatt et al., 2024 ; Litman et al., 2016 ; Sabry et al., 2021 ) shimmer, harmonics-to-noise ( Sabry et al., 2021 ) eGeMAPS features ( Fenech et al., 2022 ) |
| Video 4.3 | mutual gaze ( Kantharaju and Pelachaud, 2021 ) automatic extracted facial expression, head nods duration ( Kantharaju et al., 2020 ) | underexplored | underexploredunderexplored | saccade peak velocity ( Peechatt et al., 2024 ) | focus of attention without mutual engagement ( Sabry et al., 2021 ) facial action units from OpenFace ( Fenech et al., 2022 ) |
| Sensor 4.3 | group and individual proxemics and kinesics features ( Sabry et al., 2021 ) | bodily motion energy synchrony ( Kantharaju and Pelachaud, 2021 ) | underexplored | galvanic skin response (GSR) ( Peechatt et al., 2024 ) | traveled distance, kinetic energy, posture expansion, amount of walking, and hand gesture ( Sabry et al., 2021 ) |
| Cross-modality 4.4 | representation based leadership ( Sabry et al., 2021 ) interpersonal synchrony ( Sassier–Roublin et al., 2025 ) mutual gaze instance during interruption ( Kantharaju et al., 2020 ) MUMIN coding on social cues ( Kantharaju and Pelachaud, 2021 ) | underexplored | underexplored | underexplored | ratio between successful interruptions and speaking turns ( Sabry et al., 2021 ) |
Table 3: Features are categorized by modality and their application in group-level and individual-level collaboration analysis, highlighting current trends and potential underexplored areas in CollA research. Detailed discussions of each modality are referenced in their respective sections.
Figure 2: Human-human conversation corpus building with manual annotations is a costly process. It was particularly difficult to find corpora with direct annotations on collaboration. Our recursive approach is time-consuming, but the boundary is clearly defined, which results in a relatively grounded and complete selection for an overview of CollA corpora.
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