English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An essential aspect of prioritizing incident tickets for resolution isefficiently labeling tickets with fine-grained categories. However, ticket datais often complex and poses several unique challenges for modern machinelearning methods: (1) tickets are created and updated either by machines withpre-defined algorithms or by engineers with domain expertise that sharedifferent protocols, (2) tickets receive frequent revisions that update ticketstatus by modifying all or parts of ticket descriptions, and (3) ticketlabeling is time-sensitive and requires knowledge updates and new labels perthe rapid software and hardware improvement lifecycle. To handle these issues,we introduce Ticket- BERT which trains a simple yet robust language model forlabeling tickets using our proposed ticket datasets. Experiments demonstratethe superiority of Ticket-BERT over baselines and state-of-the-art textclassifiers on Azure Cognitive Services. We further encapsulate Ticket-BERTwith an active learning cycle and deploy it on the Microsoft IcM system, whichenables the model to quickly finetune on newly-collected tickets with a fewannotations.

Ticket-BERT: Labeling Incident Management Tickets with Language Models