Ticket-BERT: Labeling Incident Management Tickets with Language Models
This addresses the challenge of time-sensitive and complex ticket labeling for incident management systems, though it appears incremental as it adapts existing language models to a specific domain.
The paper tackled the problem of efficiently labeling incident management tickets with fine-grained categories by introducing Ticket-BERT, a language model that outperformed baselines and state-of-the-art text classifiers on Azure Cognitive Services and was deployed with an active learning cycle for quick fine-tuning.
An essential aspect of prioritizing incident tickets for resolution is efficiently labeling tickets with fine-grained categories. However, ticket data is often complex and poses several unique challenges for modern machine learning methods: (1) tickets are created and updated either by machines with pre-defined algorithms or by engineers with domain expertise that share different protocols, (2) tickets receive frequent revisions that update ticket status by modifying all or parts of ticket descriptions, and (3) ticket labeling is time-sensitive and requires knowledge updates and new labels per the rapid software and hardware improvement lifecycle. To handle these issues, we introduce Ticket- BERT which trains a simple yet robust language model for labeling tickets using our proposed ticket datasets. Experiments demonstrate the superiority of Ticket-BERT over baselines and state-of-the-art text classifiers on Azure Cognitive Services. We further encapsulate Ticket-BERT with an active learning cycle and deploy it on the Microsoft IcM system, which enables the model to quickly finetune on newly-collected tickets with a few annotations.