# DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information

arXiv: https://arxiv.org/abs/2606.30312 · HTML: https://mlsanity.com/paper/2606.30312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [mmBERT: A Modern Multilingual Encoder with Annealed Language Learning](https://mlsanity.com/paper/2509.06888/) (2025)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [Audio De-identification: A New Entity Recognition Task](https://mlsanity.com/paper/1903.07037/) (2019)
- [Named Entity Recognition for Audio De-Identification](https://mlsanity.com/paper/2204.12622/) (2022)
- [Content Anonymization for Privacy in Long-form Audio](https://mlsanity.com/paper/2510.12780/) (2025)
- [MultiGraSCCo: A Multilingual Anonymization Benchmark with Annotations of Personal Identifiers](https://mlsanity.com/paper/2603.08879/) (2026)
- [Trustera: A Live Conversation Redaction System](https://mlsanity.com/paper/2303.09438/) (2023)
- [Scalable multilingual PII annotation for responsible AI in LLMs](https://mlsanity.com/paper/2510.06250/) (2025)

## Built on by (most cited first, 1 of 1)

- [Strong Multilingual Privacy Tagging at Encoder Speed](https://mlsanity.com/paper/2609.38630/) (2026)

## Abstract (arXiv)

Conversational data collected in domains such as healthcare or social sciences is a valuable resource for research and automated analysis. However, responsible data sharing requires the detection and removal of personally identifiable and sensitive information to protect individual privacy. To support the development and evaluation of automatic de-identification systems, we present DialogPII, a multilingual dataset of synthetic dialogs and speech-derived transcripts for personal information detection. DialogPII covers eight interaction scenarios (emergency calls, medical anamnesis interviews, therapy sessions, insurance communication, customer support, clinical interviews regarding an AI-supported dashboard, police reports, and group therapy discussions), 19 entity types, and 11 languages (English, Arabic, Finnish, French, German, Hindi, Italian, Polish, Portuguese, Spanish, and Turkish). Dialogs were generated semi-automatically using large language models, manually curated for plausibility and diversity, and localized to country- and city-specific contexts. All dialogs were additionally converted to speech via text-to-speech synthesis, transcribed with Whisper, and annotated through automatic projection and manual correction, yielding aligned written and speech-derived resources across all languages. We further release baseline multilingual named entity recognition models and provide technical validation through inter-annotator agreement analysis, translation quality evaluation, annotation projection assessment, and benchmark experiments with transformer-based sequence labeling models.