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    <titleInfo>
        <title>BERT Embeddings for Automatic Readability Assessment</title>
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        <namePart type="given">Joseph</namePart>
        <namePart type="given">Marvin</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience. For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages. In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models with handcrafted linguistic features through a combined method for readability assessment. Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets, obtaining as high as 12.4% increase in F1 performance. We also show that the general information encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.69</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T BERT Embeddings for Automatic Readability Assessment
%A Imperial, Joseph Marvin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F imperial-2021-bert
%X Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience. For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages. In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models with handcrafted linguistic features through a combined method for readability assessment. Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets, obtaining as high as 12.4% increase in F1 performance. We also show that the general information encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.
%U https://aclanthology.org/2021.ranlp-1.69
%P 611-618

Markdown (Informal)

[BERT Embeddings for Automatic Readability Assessment](https://aclanthology.org/2021.ranlp-1.69) (Imperial, RANLP 2021)

• BERT Embeddings for Automatic Readability Assessment (Imperial, RANLP 2021)

ACL

• Joseph Marvin Imperial. 2021. BERT Embeddings for Automatic Readability Assessment. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 611–618, Held Online. INCOMA Ltd..