Ticket-BERT: Labeling Incident Management Tickets with Language Models
Zhexiong Liu Cris Benge Siduo Jiang
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.