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Sequence-to-Sequence Models for Automated Text Simplification
A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow reader...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334681/ http://dx.doi.org/10.1007/978-3-030-52240-7_6 |
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author | Botarleanu, Robert-Mihai Dascalu, Mihai Crossley, Scott Andrew McNamara, Danielle S. |
author_facet | Botarleanu, Robert-Mihai Dascalu, Mihai Crossley, Scott Andrew McNamara, Danielle S. |
author_sort | Botarleanu, Robert-Mihai |
collection | PubMed |
description | A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model’s capability to perform similar transformation to texts that were simplified by human experts at different levels. |
format | Online Article Text |
id | pubmed-7334681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73346812020-07-06 Sequence-to-Sequence Models for Automated Text Simplification Botarleanu, Robert-Mihai Dascalu, Mihai Crossley, Scott Andrew McNamara, Danielle S. Artificial Intelligence in Education Article A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model’s capability to perform similar transformation to texts that were simplified by human experts at different levels. 2020-06-10 /pmc/articles/PMC7334681/ http://dx.doi.org/10.1007/978-3-030-52240-7_6 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Botarleanu, Robert-Mihai Dascalu, Mihai Crossley, Scott Andrew McNamara, Danielle S. Sequence-to-Sequence Models for Automated Text Simplification |
title | Sequence-to-Sequence Models for Automated Text Simplification |
title_full | Sequence-to-Sequence Models for Automated Text Simplification |
title_fullStr | Sequence-to-Sequence Models for Automated Text Simplification |
title_full_unstemmed | Sequence-to-Sequence Models for Automated Text Simplification |
title_short | Sequence-to-Sequence Models for Automated Text Simplification |
title_sort | sequence-to-sequence models for automated text simplification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334681/ http://dx.doi.org/10.1007/978-3-030-52240-7_6 |
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