The Bot on Speaking Terms: The Effects of Conversation Architecture on Perceptions of Conversational Agents
Bibliographic record
Abstract
Conversational agents mimic natural conversation to interact with users. Since the effectiveness of interactions strongly depends on users’ perception of agents, it is crucial to design agents’ behaviors to provide the intended user perceptions. Research on human-agent and human-human communication suggests that speech specifics are associated with perceptions of communicating parties, but there is a lack of systematic understanding of how speech specifics of agents affect users’ perceptions. To address this gap, we present a framework outlining the relationships between elements of agents’ conversation architecture (dialog strategy, content affectiveness, content style and speech format) and aspects of users’ perception (interaction, ability, sociability and humanness). Synthesized based on literature reviewed from the domains of HCI, NLP and linguistics (n=57), this framework demonstrates both the identified relationships and the areas lacking empirical evidence. We discuss the implications of the framework for conversation design and highlight the inconsistencies with terminology and measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".