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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Modern deep learning models for natural language processing rely heavily on large amounts of annotated texts. However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domains. In this work, we propose a method of de-identifying free-form text documents by carefully redacting sensitive data in them. We show that our method preserves data utility for text classification, sequence labeling and question answering tasks.</abstract>
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%0 Conference Proceedings
%T Towards Task-Agnostic Privacy- and Utility-Preserving Models
%A Emelyanov, Yaroslav
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F emelyanov-2021-towards
%X Modern deep learning models for natural language processing rely heavily on large amounts of annotated texts. However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domains. In this work, we propose a method of de-identifying free-form text documents by carefully redacting sensitive data in them. We show that our method preserves data utility for text classification, sequence labeling and question answering tasks.
%U https://aclanthology.org/2021.ranlp-1.45
%P 394-401

Markdown (Informal)

[Towards Task-Agnostic Privacy- and Utility-Preserving Models](https://aclanthology.org/2021.ranlp-1.45) (Emelyanov, RANLP 2021)

• Towards Task-Agnostic Privacy- and Utility-Preserving Models (Emelyanov, RANLP 2021)

ACL

• Yaroslav Emelyanov. 2021. Towards Task-Agnostic Privacy- and Utility-Preserving Models. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 394–401, Held Online. INCOMA Ltd..