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SEACrowd/ara_close

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Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

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Tasks

readability-assessment

Citation text from the card

@inproceedings{imperial-kochmar-2023-automatic,
    title = "Automatic Readability Assessment for Closely Related Languages",
    author = "Imperial, Joseph Marvin  and
      Kochmar, Ekaterina",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.331",
    doi = "10.18653/v1/2023.findings-acl.331",
    pages = "5371--5386",
    abstract = "In recent years, the main focus of research on automatic readability assessment (ARA)     has shifted towards using expensive deep learning-based methods with the primary goal of increasing models{'} accuracy.     This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still     widely used due to the lack of existing NLP tools to extract deeper linguistic representations. In this work,     we take a step back from the technical component and focus on how linguistic aspects such as mutual intelligibility     or degree of language relatedness can improve ARA in a low-resource setting. We collect short stories written in three     languages in the Philippines{---}Tagalog, Bikol, and Cebuano{---}to train readability assessment models and explore the     interaction of data and features in vari

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dataset_licence({ "id": "SEACrowd/ara_close" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0