@inproceedings{rahman-etal-2020-mama,
    title = "Mama/Papa, Is this Text for Me?",
    author = "Rahman, Rashedur  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      {\'E}tienne, Aline  and
      Battistelli, Delphine  and
      B{\'e}chet, Nicolas  and
      Chevelu, Jonathan",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.coling-main.554/",
    doi = "10.18653/v1/2020.coling-main.554",
    pages = "6296--6301",
    abstract = "Children have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a novel method which predicts the minimal age from which a text can be understood. This method analyses each sentence of a text using a recurrent neural network, and then aggregates this information to provide the text-level prediction. Different approaches are proposed and compared to baseline models, at sentence and text levels. Experiments are carried out on a corpus of 1, 500 texts and 160K sentences. Our best model, based on LSTMs, outperforms state-of-the-art results and achieves mean absolute errors of 1.86 and 2.28, at sentence and text levels, respectively."
}