A task ontology model for domain independent dialogue management
Bibliographic record
Abstract
Dialogue systems have been a rapidly growing area in the field of human computer interaction. They can be applied in various fields, such as education, business and healthcare. Due to its complexity, the design and development of a dialogue system is time consuming and costly. It is highly desirable for a generic dialogue system, especially dialogue management that is independent of specific domains. Methods for domain independent dialogue systems have been proposed in previous research, however each of them has its own limitations. This paper presents a new approach, a task ontology model for domain independent dialogue management. An abstract task ontology is developed and based on that a generic dialogue manager is created. Knowledge about a specific task is modeled in its task ontology and retrieved by an ontology reasoning component situated in the dialogue manager. Thus the dialogue system based on this model is task or domain independent. The dialogue system has been experimented with two different tutorial tasks: the book borrowing and the online train ticket booking. The results indicate that the dialogue system can be readily applied to tasks from different domains. This paper has implications on future research and development of domain independent dialogue systems and its application in tutoring and training in virtual environments. It also contributes to the knowledge sharing and reuse of human computer interfaces.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".