# TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems

arXiv: https://arxiv.org/abs/2207.11187 · HTML: https://mlsanity.com/paper/2207.11187/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016)
- [BERTa'u: Ita'u BERT for digital customer service](https://mlsanity.com/paper/2101.12015/) (2021)
- [A Financial Service Chatbot based on Deep Bidirectional Transformers](https://mlsanity.com/paper/2003.04987/) (2020)
- [COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks](https://mlsanity.com/paper/1807.01337/) (2018)
- [Analyzing HPC Support Tickets: Experience and Recommendations](https://mlsanity.com/paper/2010.04321/) (2020)
- [A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks](https://mlsanity.com/paper/2104.07944/) (2021)
- [Hi, how can I help you?: Automating enterprise IT support help desks](https://mlsanity.com/paper/1711.02012/) (2017)
- [Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts](https://mlsanity.com/paper/1807.02854/) (2018)

## Built on by (most cited first, 7 of 7)

- [Ticket-BERT: Labeling Incident Management Tickets with Language Models](https://mlsanity.com/paper/2307.00108/) (2023)
- [TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications](https://mlsanity.com/paper/2601.00691/) (2026)
- [Comparison of Machine Learning Methods for Assigning Software Issues to Team Members](https://mlsanity.com/paper/2307.00009/) (2023)
- [Intent Detection at Scale: Tuning a Generic Model using Relevant Intents](https://mlsanity.com/paper/2309.08647/) (2023)
- [AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML](https://mlsanity.com/paper/2406.01789/) (2024)
- [A Scalable and High Availability Solution for Recommending Resolutions to Problem Tickets](https://mlsanity.com/paper/2507.19846/) (2025)
- [Comparison of Information Retrieval Techniques Applied to IT Support Tickets](https://mlsanity.com/paper/2508.05654/) (2025)

## Abstract (arXiv)

This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.