Changing the World by Changing the Data

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


    NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts. Algorithmic solutions have so far had limited success. An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals. This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world. The question is only how much thought we want to invest into that process.
    Original languageEnglish
    Title of host publicationProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
    Number of pages13
    Place of PublicationOnline
    PublisherAssociation for Computational Linguistics
    Publication date1 Aug 2021
    Publication statusPublished - 1 Aug 2021


    • Natural Language Processing
    • Deep Learning Models
    • Data Curation
    • Social Biases
    • Algorithmic Solutions


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