Personal information leakage detection in conversations

Qiongkai Xu, Lizhen Qu, Zeyu Gao, Gholamreza Haffari

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


The global market size of conversational assistants (chatbots) is expected to grow to USD 9.4 billion by 2024, according to Marketsand-Markets. Despite the wide use of chatbots, leakage of personal information through chatbots poses serious privacy concerns for their users. In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants. The detection task is formulated as an alignment optimization problem and a new dataset PERSONA-LEAKAGE is collected for evaluation. In this paper, we propose two novel constrained alignment models, which consistently outperform baseline methods on PERSONA-LEAKAGE. Moreover, we conduct analysis on the behavior of recently proposed personalized chit-chat dialogue systems. The empirical results show that those systems suffer more from personal information disclosure than the widely used Seq2Seq model and the language model. In those cases, a significant number of information leaking utterances can be detected by our models with high precision.

Original languageEnglish
Title of host publicationEMNLP 2020, 2020 Conference on Empirical Methods in Natural Language Processing
Subtitle of host publicationProceedings of the Conference
EditorsTrevor Cohn, Yulan He, Yang Liu
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages14
ISBN (Electronic)9781952148606
Publication statusPublished - 2020
EventEmpirical Methods in Natural Language Processing 2020 - Virtual, Punta Cana, Dominican Republic
Duration: 16 Nov 202020 Nov 2020 (Website) (Proceedings)


ConferenceEmpirical Methods in Natural Language Processing 2020
Abbreviated titleEMNLP 2020
Country/TerritoryDominican Republic
CityPunta Cana
Internet address

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