DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information
arXiv:2606.30312v1 Announce Type: new 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
The increasing deployment of AI in conversational systems and the growing need for robust privacy protection are driving the demand for sophisticated de-identification tools.
This dataset directly addresses a critical hurdle in responsible AI deployment by enabling better PII detection, which is essential for data sharing and privacy compliance in sensitive domains.
The availability of a multilingual synthetic dataset for PII detection will accelerate the development and evaluation of de-identification systems, making responsible data usage more feasible.
- · AI developers
- · Healthcare sector
- · Social sciences researchers
- · Privacy tech companies
- · Data privacy violators
- · Systems with weak de-identification
Improved accuracy and robustness of PII detection models for conversational AI.
Increased ability for organizations to share and utilize sensitive conversational data while maintaining privacy.
Enhanced trust in AI systems handling personal information, potentially accelerating broader AI adoption in regulated industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL