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

2026-06-29

AI Summary

This work addresses the challenge of detecting sensitive personally identifiable information in conversational data from privacy-sensitive domains such as healthcare and social sciences. To support privacy-preserving and responsible data sharing, the authors construct a multilingual synthetic dialogue dataset spanning eight scenarios, nineteen entity types, and eleven languages. Dialogues are generated using large language models, manually validated, synthesized into speech, and transcribed with Whisper to create aligned text–speech pairs. The study innovatively combines automatic annotation projection with human correction to achieve fine-grained cross-modal labeling. The released dataset includes high-quality annotations and a Transformer-based baseline named entity recognition model. Experimental evaluation through inter-annotator agreement, translation quality, and benchmark performance demonstrates the dataset’s validity and practical utility.

Abstract

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.

Problem

• personal information detection
• de-identification
• multilingual dataset
• conversational data
• privacy protection

Innovation

• synthetic dialog dataset
• multilingual PII detection
• speech-derived transcripts
• annotation projection
• large language model generation