Multilingual adaptative text simplification
Reading is an essential skill that plays a crucial role in our daily lives. It allows us to access information, gain knowledge, expand our understanding of the world around us, and build the foundation for learning, communication, and personal growth. However, many texts we encounter day after day o...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/689317 |
| Acceso en línea: | http://hdl.handle.net/10803/689317 |
| Access Level: | acceso abierto |
| Palabra clave: | Adaptive text simplification Lexical simplification Sentence simplification Complex word identification Controllable lexical and sentence simplification Simplificació adaptativa de text Simplificació lèxica Simplificació de frases Identificació de paraules complexes Simplificació léxica i oracional controlable 62 |
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| dc.title.none.fl_str_mv |
Multilingual adaptative text simplification |
| title |
Multilingual adaptative text simplification |
| spellingShingle |
Multilingual adaptative text simplification Sheang, Kim Cheng Adaptive text simplification Lexical simplification Sentence simplification Complex word identification Controllable lexical and sentence simplification Simplificació adaptativa de text Simplificació lèxica Simplificació de frases Identificació de paraules complexes Simplificació léxica i oracional controlable 62 |
| title_short |
Multilingual adaptative text simplification |
| title_full |
Multilingual adaptative text simplification |
| title_fullStr |
Multilingual adaptative text simplification |
| title_full_unstemmed |
Multilingual adaptative text simplification |
| title_sort |
Multilingual adaptative text simplification |
| dc.creator.none.fl_str_mv |
Sheang, Kim Cheng |
| author |
Sheang, Kim Cheng |
| author_facet |
Sheang, Kim Cheng |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Saggion, Horacio Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions |
| dc.subject.none.fl_str_mv |
Adaptive text simplification Lexical simplification Sentence simplification Complex word identification Controllable lexical and sentence simplification Simplificació adaptativa de text Simplificació lèxica Simplificació de frases Identificació de paraules complexes Simplificació léxica i oracional controlable 62 |
| topic |
Adaptive text simplification Lexical simplification Sentence simplification Complex word identification Controllable lexical and sentence simplification Simplificació adaptativa de text Simplificació lèxica Simplificació de frases Identificació de paraules complexes Simplificació léxica i oracional controlable 62 |
| description |
Reading is an essential skill that plays a crucial role in our daily lives. It allows us to access information, gain knowledge, expand our understanding of the world around us, and build the foundation for learning, communication, and personal growth. However, many texts we encounter day after day often contain complex words or syntactic structures that can cause reading difficulties for certain groups of people; this motivates the need for Automatic Text Simplification (ATS). ATS is a Natural Language Processing (NLP) task that aims to reduce the linguistic complexity of a text while preserving its original information and meaning. It involves various operations, such as replacing complex words with simpler synonyms, splitting long sentences into shorter ones, and reorganizing the structure of the text. The goal of ATS is to make texts more accessible and understandable to a broader audience, including non-native speakers, children, and individuals with Dyslexia, Autism, Aphasia, Intellectual Disabilities, and Deaf and Hard of Hearing. In this work, we will discuss our proposed methods for Complex Word Identification (CWI), Lexical Simplification (LS), and Sentence Simplification (SS) in order to help improve reading comprehension. For CWI, we propose several systems based on different machine learning algorithms, such as Convolutional Neural Networks, CatBoost, and XGBoost with word embeddings and feature-engineered for identifying complex words in English, Spanish, German, and French texts. For LS, we propose two systems, monolingual English and multilingual system supporting English, Spanish, and Portuguese. For SS, we propose several systems to simplify English and Spanish texts. In both LS and SS, we explore the use of transfer learning and controllable mechanism, where the transfer learning help create the model that requires less amount of training data, and the controllable mechanism gives us the ability to adjust the outputs based on our preference, especially for different target audiences. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023 2023 |
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info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion |
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doctoralThesis |
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publishedVersion |
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http://hdl.handle.net/10803/689317 |
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http://hdl.handle.net/10803/689317 |
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Inglés |
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Inglés |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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openAccess |
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184 p. application/pdf |
| dc.publisher.none.fl_str_mv |
Universitat Pompeu Fabra |
| publisher.none.fl_str_mv |
Universitat Pompeu Fabra |
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TDX (Tesis Doctorals en Xarxa) reponame:TDR. Tesis Doctorales en Red instname:CBUC, CESCA |
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CBUC, CESCA |
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TDR. Tesis Doctorales en Red |
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TDR. Tesis Doctorales en Red |
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1869404900826284032 |
| spelling |
Multilingual adaptative text simplificationSheang, Kim ChengAdaptive text simplificationLexical simplificationSentence simplificationComplex word identificationControllable lexical and sentence simplificationSimplificació adaptativa de textSimplificació lèxicaSimplificació de frasesIdentificació de paraules complexesSimplificació léxica i oracional controlable62Reading is an essential skill that plays a crucial role in our daily lives. It allows us to access information, gain knowledge, expand our understanding of the world around us, and build the foundation for learning, communication, and personal growth. However, many texts we encounter day after day often contain complex words or syntactic structures that can cause reading difficulties for certain groups of people; this motivates the need for Automatic Text Simplification (ATS). ATS is a Natural Language Processing (NLP) task that aims to reduce the linguistic complexity of a text while preserving its original information and meaning. It involves various operations, such as replacing complex words with simpler synonyms, splitting long sentences into shorter ones, and reorganizing the structure of the text. The goal of ATS is to make texts more accessible and understandable to a broader audience, including non-native speakers, children, and individuals with Dyslexia, Autism, Aphasia, Intellectual Disabilities, and Deaf and Hard of Hearing. In this work, we will discuss our proposed methods for Complex Word Identification (CWI), Lexical Simplification (LS), and Sentence Simplification (SS) in order to help improve reading comprehension. For CWI, we propose several systems based on different machine learning algorithms, such as Convolutional Neural Networks, CatBoost, and XGBoost with word embeddings and feature-engineered for identifying complex words in English, Spanish, German, and French texts. For LS, we propose two systems, monolingual English and multilingual system supporting English, Spanish, and Portuguese. For SS, we propose several systems to simplify English and Spanish texts. In both LS and SS, we explore the use of transfer learning and controllable mechanism, where the transfer learning help create the model that requires less amount of training data, and the controllable mechanism gives us the ability to adjust the outputs based on our preference, especially for different target audiences.La lectura és una habilitat essencial que juga un paper crucial en la nostra vida quotidiana. La lectura ens permet accedir a la informació, adquirir coneixements, ampliar la nostra comprensió del món que ens envolta i construir les bases per a l'aprenentatge, la comunicació, i creixement personal. No obstant això, molts textos sovint contenen paraules complexes o estructures sintàctiques que poden provocar dificultats lectores per a determinats grups de persones; això motiva la necessitat de la simplificació automàtica de text (ATS). ATS es una tasca que pretén reduir la complexitat lingüística d'un text tot conservant la seva informació i significat originals. Implica diversos operacions, com ara substituir paraules complexes per sinònims més senzills, dividir les frases llargues en frases més curtes i reorganitzant l'estructura del text. L'objectiu d'ATS és fer que els textos siguin més accessibles i entenedors a un públic més ampli. En aquest treball, presentem nostra proposta de mètodes d'identificació de paraules complexes (CWI), simplificació lèxica (LS) i Simplificació de frases (SS) per tal de fer els textos més accessibles. Pel que fa la CWI, proposem diversos sistemes basats en algorismes d'aprenentatge automàtic, com ara xarxes neuronals de convolucions, “CatBoost” i “XGBoost” amb incrustacions de paraules i característiques dissenyades per identificar paraules complexes en anglès, espanyol, alemany i francès. Pel que fa la LS, proposem dos sistemes, un pel anglès i un multilingüe. Per a la SS, explorem l'ús de l'aprenentatge de transferència i el mecanismes de control, on l'aprenentatge de transferència ajuda a crear un model que requereix menys quantitat de dades d'entrenament mentre que el mecanisme de control ens dona la capacitat per ajustar les sortides en funció de la nostra preferència, especialment per a diferents públics objectiu.Programa de Doctorat en Tecnologies de la Informació i les ComunicacionsUniversitat Pompeu FabraSaggion, HoracioUniversitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions202320232023info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion184 p.application/pdfhttp://hdl.handle.net/10803/689317TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésL'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/6893172026-06-14T12:46:07Z |
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