# Ticket-BERT: Labeling Incident Management Tickets with Language Models

arXiv: https://arxiv.org/abs/2307.00108 · HTML: https://mlsanity.com/paper/2307.00108/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks](https://mlsanity.com/paper/1807.07425/) (2018)
- [A Survey on Neural Network Language Models](https://mlsanity.com/paper/1906.03591/) (2019)
- [Context based Text-generation using LSTM networks](https://mlsanity.com/paper/2005.00048/) (2020)
- [TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems](https://mlsanity.com/paper/2207.11187/) (2022)
- [Modeling Rich Contexts for Sentiment Classification with LSTM](https://mlsanity.com/paper/1605.01478/) (2016)
- [Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers](https://mlsanity.com/paper/2103.15822/) (2021)

## Built on by (most cited first, 3 of 3)

- [TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications](https://mlsanity.com/paper/2601.00691/) (2026)
- [Comparison of Information Retrieval Techniques Applied to IT Support Tickets](https://mlsanity.com/paper/2508.05654/) (2025)
- [Designing AI Pipelines for Decision-Ready ITSM Intelligence](https://mlsanity.com/paper/2608.12670/) (2026)

## Abstract (arXiv)

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.