• Login
    View Item 
    •   Home
    • Research
    • Preprints
    • View Item
    •   Home
    • Research
    • Preprints
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Attention-Aware Answers of the Crowd

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    475.5Kb
    Format:
    PDF
    Description:
    Pre-print
    Download
    Type
    Preprint
    Authors
    Tu, Jingzheng
    Yu, Guoxian
    Wang, Jun
    Domeniconi, Carlotta
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2019-12-24
    Permanent link to this record
    http://hdl.handle.net/10754/661042
    
    Metadata
    Show full item record
    Abstract
    Crowdsourcing is a relatively economic and efficient solution to collect annotations from the crowd through online platforms. Answers collected from workers with different expertise may be noisy and unreliable, and the quality of annotated data needs to be further maintained. Various solutions have been attempted to obtain high-quality annotations. However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks). In practice, workers' attention level changes over time, and the ignorance of which can affect the reliability of the annotations. In this paper, we focus on a novel and realistic crowdsourcing scenario involving attention-aware annotations. We propose a new probabilistic model that takes into account workers' attention to estimate the label quality. Expectation propagation is adopted for efficient Bayesian inference of our model, and a generalized Expectation Maximization algorithm is derived to estimate both the ground truth of all tasks and the label-quality of each individual crowd worker with attention. In addition, the number of tasks best suited for a worker is estimated according to changes in attention. Experiments against related methods on three real-world and one semi-simulated datasets demonstrate that our method quantifies the relationship between workers' attention and label-quality on the given tasks, and improves the aggregated labels.
    Citation
    Tu, J., Yu, G., Wang, J., Domeniconi, C., & Zhang, X. (2020). Attention-Aware Answers of the Crowd. Proceedings of the 2020 SIAM International Conference on Data Mining, 451–459. doi:10.1137/1.9781611976236.51
    Publisher
    arXiv
    DOI
    10.1137/1.9781611976236.51
    arXiv
    1912.11238
    Additional Links
    https://arxiv.org/pdf/1912.11238
    ae974a485f413a2113503eed53cd6c53
    10.1137/1.9781611976236.51
    Scopus Count
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.