A Hybrid Continual Learning Approach for Efficient Hierarchical Classification of IT Support Tickets in the Presence of Class Overlap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A support ticket describes an issue faced by a system's end-users when they encounter issues with their system. For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of classifying support tickets is vital to guarantee long-term clients. Due to the complexity of the unstructured nature of human language, text classification is challenging. The task is even harder when classes overlap. In the business world, an efficient and interpretable ML model is preferred over an expensive black-box model. In this paper, we propose a Hybrid Online Offline Model (HOOM) for efficient classification of hierarchical text documents using linear ML models. The experimental results on a private dataset of IT support tickets show that the hybrid model (HOOM) exhibits a promising performance if deployed in a real-world scenario. Furthermore, the hybrid model is anticipated to have a fast inference time given the underlying linear classifiers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it