@inproceedings{wilkens-etal-2024-exploring,
title = "Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers",
author = "Wilkens, Rodrigo and
Watrin, Patrick and
Cardon, R{\'e}mi and
Pintard, Alice and
Gribomont, Isabelle and
Fran{\c{c}}ois, Thomas",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.153",
pages = "2316--2331",
abstract = "Linguistic features have a strong contribution in the context of the automatic assessment of text readability (ARA). They have been one of the anchors between the computational and theoretical models. With the development in the ARA field, the research moved to Deep Learning (DL). In an attempt to reconcile the mixed results reported in this context, we present a systematic comparison of 6 hybrid approaches along with standard Machine Learning and DL approaches, on 4 corpora (different languages and target audiences). The various experiments clearly highlighted two rather simple hybridization methods (soft label and simple concatenation). They also appear to be the most robust on smaller datasets and across various tasks and languages. This study stands out as the first to systematically compare different architectures and approaches to feature hybridization in DL, as well as comparing performance in terms of two languages and two target audiences of the text, which leads to a clearer pattern of results.",
}
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<abstract>Linguistic features have a strong contribution in the context of the automatic assessment of text readability (ARA). They have been one of the anchors between the computational and theoretical models. With the development in the ARA field, the research moved to Deep Learning (DL). In an attempt to reconcile the mixed results reported in this context, we present a systematic comparison of 6 hybrid approaches along with standard Machine Learning and DL approaches, on 4 corpora (different languages and target audiences). The various experiments clearly highlighted two rather simple hybridization methods (soft label and simple concatenation). They also appear to be the most robust on smaller datasets and across various tasks and languages. This study stands out as the first to systematically compare different architectures and approaches to feature hybridization in DL, as well as comparing performance in terms of two languages and two target audiences of the text, which leads to a clearer pattern of results.</abstract>
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%0 Conference Proceedings
%T Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers
%A Wilkens, Rodrigo
%A Watrin, Patrick
%A Cardon, Rémi
%A Pintard, Alice
%A Gribomont, Isabelle
%A François, Thomas
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F wilkens-etal-2024-exploring
%X Linguistic features have a strong contribution in the context of the automatic assessment of text readability (ARA). They have been one of the anchors between the computational and theoretical models. With the development in the ARA field, the research moved to Deep Learning (DL). In an attempt to reconcile the mixed results reported in this context, we present a systematic comparison of 6 hybrid approaches along with standard Machine Learning and DL approaches, on 4 corpora (different languages and target audiences). The various experiments clearly highlighted two rather simple hybridization methods (soft label and simple concatenation). They also appear to be the most robust on smaller datasets and across various tasks and languages. This study stands out as the first to systematically compare different architectures and approaches to feature hybridization in DL, as well as comparing performance in terms of two languages and two target audiences of the text, which leads to a clearer pattern of results.
%U https://aclanthology.org/2024.findings-eacl.153
%P 2316-2331
Markdown (Informal)
[Exploring hybrid approaches to readability: experiments on the complementarity between linguistic features and transformers](https://aclanthology.org/2024.findings-eacl.153) (Wilkens et al., Findings 2024)
ACL