 [2306.01457] Driving Context into Text-to-Text Privatization




























  








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Computer Science > Computation and Language


arXiv:2306.01457 (cs)
    




  [Submitted on 2 Jun 2023]
Title:Driving Context into Text-to-Text Privatization
Authors:Stefan Arnold, Dilara Yesilbas, Sven Weinzierl View a PDF of the paper titled Driving Context into Text-to-Text Privatization, by Stefan Arnold and 2 other authors
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Abstract:\textit{Metric Differential Privacy} enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search. Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as \textit{'bank'}. To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection. We encompass our modification to the privatization mechanism with an estimation of privacy and utility. For word sense disambiguation on the \textit{Words in Context} dataset, we demonstrate a substantial increase in classification accuracy by 6.05%.
    



Subjects:

Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:
arXiv:2306.01457 [cs.CL]


 
(or 
arXiv:2306.01457v1 [cs.CL] for this version)
          


 
 https://doi.org/10.48550/arXiv.2306.01457


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                  arXiv-issued DOI via DataCite







Submission history From: Stefan Arnold [view email]       [v1]
        Fri, 2 Jun 2023 11:33:06 UTC (125 KB)



 

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