Few-Shot User Intent Detection and Response Selection for Conversational Dialogue System Using Deep Learning
Bibliographic record
Abstract
Conversational dialogue systems (CDSs), also known as conversational agents, have made significant development in recent years, driven by advances in natural language processing, machine learning, and artificial intelligence techniques. As a result, CDSs have been implemented across various industries, including education, e-commerce, and customer service in messaging apps, websites, and mobile apps to engage with users through natural language. The primary objective of chatbots is to facilitate communication with people and make numerous repetitious tasks easier for humans. This thesis investigates the application of deep learning methodologies in enterprise CDSs to enhance interpretability, fostering user trust in decision-making processes. The contributions of this thesis include proposing example and description-driven approaches that focus on the semantic similarities between the user input and the intent examples or descriptions in a topological tree for few-shot intent detection in enterprise CDSs. Moreover, this thesis presents a novel Topic-Aware Response Selection (TARS) model to retrieve the most suitable and coherent response from a set of candidates based on contextual information for users in persona-based CDSs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".