# Universal Model in Online Customer Service

arXiv: https://arxiv.org/abs/2402.15666 · HTML: https://mlsanity.com/paper/2402.15666/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Harvesting and Refining Question-Answer Pairs for Unsupervised QA](https://mlsanity.com/paper/2005.02925/) (2020)
- [End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension](https://mlsanity.com/paper/1610.09996/) (2016)
- [Matching Questions and Answers in Dialogues from Online Forums](https://mlsanity.com/paper/2005.09276/) (2020)

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

- [REIC: RAG-Enhanced Intent Classification at Scale](https://mlsanity.com/paper/2506.00210/) (2025)
- [Improving Customer Service with Automatic Topic Detection in User Emails](https://mlsanity.com/paper/2502.19115/) (2025)
- [Don't Shoot The Breeze: Topic Continuity Model Using Nonlinear Naive Bayes With Attention](https://mlsanity.com/paper/2602.09312/) (2026)

## Abstract (arXiv)

Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predict-ing labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.