@inproceedings{le-etal-2018-joint,
    title = "Joint learning of frequency and word embeddings for multilingual readability assessment",
    author = "Le, Dieu-Thu  and
      Nguyen, Cam-Tu  and
      Wang, Xiaoliang",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Hsin-Hsi  and
      Ng, Vincent  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-3714/",
    doi = "10.18653/v1/W18-3714",
    pages = "103--107",
    abstract = "This paper describes two models that employ word frequency embeddings to deal with the problem of readability assessment in multiple languages. The task is to determine the difficulty level of a given document, i.e., how hard it is for a reader to fully comprehend the text. The proposed models show how frequency information can be integrated to improve the readability assessment. The experimental results testing on both English and Chinese datasets show that the proposed models improve the results notably when comparing to those using only traditional word embeddings."
}