A Joint Model for Multimodal Document Quality Assessment
Abstract
The quality of a document is affected by various factors, including grammaticality, 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 quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.
Cite
@article{arxiv.1901.01010,
title = {A Joint Model for Multimodal Document Quality Assessment},
author = {Aili Shen and Bahar Salehi and Timothy Baldwin and Jianzhong Qi},
journal= {arXiv preprint arXiv:1901.01010},
year = {2019}
}