NUIG-DSI’s submission to The GEM Benchmark 2021

    Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

    Abstract

    This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and CommonGen. We follow an approach similar to the one described in the GEM benchmark paper where we use the pre-trained T5-base model for our submission. We train this model on additional monolingual data where we experiment with different masking strategies specifically focused on masking entities, predicates and concepts as well as a random masking strategy for pre-training. In our results we find that random masking performs the best in terms of automatic evaluation metrics, though the results are not statistically significantly different compared to other masking strategies.

    Original languageEnglish
    Title of host publicationGEM 2021 - 1st Workshop on Natural Language Generation, Evaluation, and Metrics, Proceedings
    EditorsAntoine Bosselut, Esin Durmus, Varun Prashant Gangal, Sebastian Gehrmann, Yacine Jernite, Laura Perez-Beltrachini, Samira Shaikh, Wei Xu
    PublisherAssociation for Computational Linguistics (ACL)
    Pages148-154
    Number of pages7
    ISBN (Electronic)9781954085671
    Publication statusPublished - 2021
    Event1st Workshop on Natural Language Generation, Evaluation, and Metrics, GEM 2021 - Virtual, Online, Thailand
    Duration: 5 Aug 20216 Aug 2021

    Publication series

    NameGEM 2021 - 1st Workshop on Natural Language Generation, Evaluation, and Metrics, Proceedings

    Conference

    Conference1st Workshop on Natural Language Generation, Evaluation, and Metrics, GEM 2021
    Country/TerritoryThailand
    CityVirtual, Online
    Period5/08/216/08/21

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