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    A multimodal approach to assessing document quality

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    Author
    Shen, A; Salehi, B; Qi, J; Baldwin, T
    Date
    2020-07-22
    Source Title
    Journal of Artificial Intelligence Research
    Publisher
    AI Access Foundation
    University of Melbourne Author/s
    Salehi, Bahar; Baldwin, Timothy; Qi, Jianzhong
    Affiliation
    Computing and Information Systems
    Metadata
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    Document Type
    Journal Article
    Citations
    Shen, A., Salehi, B., Qi, J. & Baldwin, T. (2020). A multimodal approach to assessing document quality. Journal of Artificial Intelligence Research, 68, pp.607-632. https://doi.org/10.1613/JAIR.1.11647.
    Access Status
    Access this item via the Open Access location
    URI
    http://hdl.handle.net/11343/254262
    DOI
    10.1613/JAIR.1.11647
    Open Access URL
    http://doi.org/10.1613/JAIR.1.11647
    Abstract
    <jats:p>&#x0D; &#x0D; &#x0D; The perceived quality of a document is affected by various factors, including grammat- icality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text — such as images, font choices, and visual layout — we propose a joint model that combines the text content with a visual rendering of the document for document qual- ity assessment. Our joint model achieves state-of-the-art results over five datasets in two domains (Wikipedia and academic papers), which demonstrates the complementarity of textual and visual features, and the general applicability of our model. To examine what kinds of features our model has learned, we further train our model in a multi-task learning setting, where document quality assessment is the primary task and feature learning is an auxiliary task. Experimental results show that visual embeddings are better at learning structural features while textual embeddings are better at learning readability scores, which further verifies the complementarity of visual and textual features.&#x0D; &#x0D; &#x0D; </jats:p>

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