@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Mama/Papa, Is this Text for Me?
%A Rahman, Rashedur
%A Lecorvé, Gwénolé
%A Étienne, Aline
%A Battistelli, Delphine
%A Béchet, Nicolas
%A Chevelu, Jonathan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F rahman-etal-2020-mama
%X 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.
%R 10.18653/v1/2020.coling-main.554
%U https://aclanthology.org/2020.coling-main.554
%U https://doi.org/10.18653/v1/2020.coling-main.554
%P 6296-6301
Markdown (Informal)
[Mama/Papa, Is this Text for Me?](https://aclanthology.org/2020.coling-main.554) (Rahman et al., COLING 2020)
ACL
- Rashedur Rahman, Gwénolé Lecorvé, Aline Étienne, Delphine Battistelli, Nicolas Béchet, and Jonathan Chevelu. 2020. Mama/Papa, Is this Text for Me?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6296–6301, Barcelona, Spain (Online). International Committee on Computational Linguistics.