# Reliable and Interpretable Drift Detection in Streams of Short Texts

arXiv: https://arxiv.org/abs/2305.17750 · HTML: https://mlsanity.com/paper/2305.17750/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 10 of 10)

- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://mlsanity.com/paper/2102.01454/) (2021)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)
- [Automatically detecting data drift in machine learning classifiers](https://mlsanity.com/paper/2111.05672/) (2021)
- [Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora](https://mlsanity.com/paper/2211.16259/) (2022)
- [Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.05158/) (2022)

## Built on by (most cited first, 2 of 2)

- [You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2506.11103/) (2025)
- [Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting](https://mlsanity.com/paper/2610.17399/) (2026)

## Abstract (arXiv)

Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.