[Paper] TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications

Source: arXiv - 2601.00691v1

Overview

The paper TeleDoCTR tackles a pain point that many large telecom operators face every day: turning a flood of support tickets into fast, accurate resolutions. By combining domain‑specific ranking with modern generative AI, the authors build an end‑to‑end system that automatically routes tickets, pulls up the most relevant past cases, and drafts a fault‑analysis report—all without a human having to sift through mountains of documentation.

Key Contributions

• Domain‑tailored architecture: A unified pipeline that couples a classification model (ticket routing), a retrieval engine (similar‑ticket search), and a generation model (fault‑analysis report) specifically trained on telecom data.
• Contextual ranking: Introduces a domain‑specific ranking loss that boosts the relevance of retrieved historical tickets beyond generic semantic similarity.
• Generative fault analysis: Leverages a fine‑tuned language model to produce structured reports (issue description, root cause, suggested fix) that are directly usable by engineers.
• Real‑world evaluation: Experiments on a proprietary telecom‑infrastructure dataset show consistent gains over strong baselines in classification accuracy, retrieval recall, and report quality (BLEU/ROUGE).
• Operational efficiency gains: Demonstrates a measurable reduction in mean time‑to‑resolution (MTTR) when the system is deployed in a pilot environment.

Methodology

1. Data preparation – The authors collected ~120 k tickets spanning network outages, hardware failures, and service degradations, along with associated expert‑written resolution notes.
2. Ticket routing (classification) – A transformer‑based classifier (e.g., BERT‑large) is fine‑tuned to predict the responsible expert team (≈ 30 categories).
3. Contextual retrieval – They index historical tickets with dense embeddings (SBERT) and augment them with a domain‑specific relevance scorer that incorporates telecom ontology terms (e.g., “BGP flap”, “OLT failure”). The final score is a weighted blend of semantic similarity and ontology overlap.
4. Report generation – A sequence‑to‑sequence model (T5‑base) is further fine‑tuned on pairs of ticket text + expert resolution notes. The model is prompted to output three sections: Problem Summary, Root Cause, and Recommended Action.
5. End‑to‑end integration – The pipeline runs in real time: a new ticket is first classified, then the top‑k similar tickets are fetched, and finally the generation module produces a draft report that can be edited by a human if needed.

All components are trained and evaluated using standard metrics (accuracy, MAP@10, BLEU, ROUGE) and a custom “resolution‑time” metric that captures operational impact.

Results & Findings

Ablation studies show that removing the domain‑specific ranking term drops MAP@10 by ~12 %, and swapping the generative model for a generic GPT‑2 reduces BLEU by ~9 %. Human evaluators rated TeleDoCTR’s draft reports as “ready for hand‑off” in 78 % of cases, compared to 45 % for the baseline.

Practical Implications

• Faster ticket triage – Automated routing cuts down the manual effort of assigning tickets, freeing up senior engineers to focus on complex cases.
• Knowledge reuse – By surfacing the most relevant past incidents, the system reduces duplicated effort and helps newer staff learn from historical fixes.
• Draft reports as a starting point – Engineers can edit a concise, AI‑generated analysis instead of writing from scratch, accelerating the resolution workflow.
• Scalable to other domains – The modular design (classification + retrieval + generation) can be re‑trained on any industry with rich ticket histories (e.g., cloud services, ITIL support).
• Cost reduction – Preliminary ROI calculations suggest a 15–20 % reduction in support labor costs for a mid‑size telecom operator after a 3‑month adoption period.

Limitations & Future Work

• Data privacy & bias – The model is trained on proprietary tickets; extending it to multi‑tenant environments will require robust anonymization and bias‑mitigation strategies.
• Domain shift – Rapidly evolving telecom standards (5G, edge computing) may outpace the static ontology used for ranking, necessitating continual updates.
• Human‑in‑the‑loop validation – While the generated reports are high‑quality, the system still relies on expert review for safety‑critical incidents.
• Future directions proposed by the authors include:
  1. Incorporating real‑time network telemetry as additional context.
  2. Exploring reinforcement learning to optimize the end‑to‑end MTTR objective.
  3. Extending the pipeline to multilingual ticket streams common in global operators.

Authors

• Mohamed Trabelsi
• Huseyin Uzunalioglu

Paper Information

• arXiv ID: 2601.00691v1
• Categories: cs.LG, cs.CL, cs.IR
• Published: January 2, 2026
• PDF: Download PDF