The Corpus of Basque Simplified Texts (CBST)

In this paper we present the corpus of Basque simplified texts. This corpus compiles 227 original sentences of science popularisation domain and two simplified versions of each sentence. The simplified versions have been created following different approaches: the structural, by a court translator who considers easy-to-read guidelines and the intuitive, by a teacher based on her experience. The aim of this corpus is to make a comparative analysis of simplified text. To that end, we also present the annotation scheme we have created to annotate the corpus. The annotation scheme is divided into eight macro-operations: delete, merge, split, transformation, insert, reordering, no operation and other. These macro-operations can be classified into different operations. We also relate our work and results to other languages. This corpus will be used to corroborate the decisions taken and to improve the design of the automatic text simplification system for Basque.Cerrar texto de financiación Itziar Gonzalez-Dios's work was funded by a Ph.D. grant from the Basque Government and a postdoctoral grant for the new doctors from the Vice-rectory of Research of the University of the Basque Country (UPV/EHU). We are very grateful to the translator and teacher that simplified the texts. We also want to thank Dominique Brunato, Felice Dell'Orletta and Giulia Venturi for their help with the Italian annotation scheme and their suggestions when analysing the corpus and Oier Lopez de Lacalle for his help with the statistical analysis. We also want to express our gratitude to the anonymous reviewers for their comments and suggestions. This research was supported by the Basque Government (IT344-10), and the Spanish Ministry of Economy and Competitiveness, EXTRECM Project (TIN2013-46616-C2-1-R)

The Corpus of Basque Simplified Texts (CBST)

In this paper we present the corpus of Basque simplified texts. This corpus compiles 227 original sentences of science popularisation domain and two simplified versions of each sentence. The simplified versions have been created following different approaches: the structural, by a court translator who considers easy-to-read guidelines and the intuitive, by a teacher based on her experience. The aim of this corpus is to make a comparative analysis of simplified text. To that end, we also present the annotation scheme we have created to annotate the corpus. The annotation scheme is divided into eight macro-operations: delete, merge, split, transformation, insert, reordering, no operation and other. These macro-operations can be classified into different operations. We also relate our work and results to other languages. This corpus will be used to corroborate the decisions taken and to improve the design of the automatic text simplification system for Basque.Cerrar texto de financiación Itziar Gonzalez-Dios's work was funded by a Ph.D. grant from the Basque Government and a postdoctoral grant for the new doctors from the Vice-rectory of Research of the University of the Basque Country (UPV/EHU). We are very grateful to the translator and teacher that simplified the texts. We also want to thank Dominique Brunato, Felice Dell'Orletta and Giulia Venturi for their help with the Italian annotation scheme and their suggestions when analysing the corpus and Oier Lopez de Lacalle for his help with the statistical analysis. We also want to express our gratitude to the anonymous reviewers for their comments and suggestions. This research was supported by the Basque Government (IT344-10), and the Spanish Ministry of Economy and Competitiveness, EXTRECM Project (TIN2013-46616-C2-1-R)

Improving Computer-Assisted Language Learning through Hierarchical Knowledge Structures

A common drawback in traditional language education is that all students in the same class use the same content. Since students may have different backgrounds such as prior knowledge and learning speed, one single curriculum may not be able to accommodate every student. Unfortunately, most students cannot afford personalized language learning, since preparing personalized learning content can be very time-consuming and potentially requires a significant amount of expert labor. Recently, researchers have proposed automatic systems to assist language education, such as Computer-based Assessment Systems (CAT) and Intelligent Tutoring Systems (ITS). However, previous work usually characterizes the student's knowledge and the difficulty of learning content using numeric scores, which may not be comprehensive. To improve on this, this thesis introduces hierarchical knowledge structures to assist in multiple tasks in language education. First, this structure multidimensionally characterizes the difficulty of each learning material by its relative difficulty to other materials and models the whole corpus with a graph structure. Additionally, we can utilize the hierarchical knowledge structure to multidimensionally assess a student's prior knowledge, predict the student's future performance on a specific task, and recommend learning content that is appropriate for each student. Furthermore, the hierarchical knowledge structure enables us to build a framework to characterize existing learning curricula extracted from textbooks and online learning tools, and apply expert wisdom that we have discovered to automatically design learning curricula. The hierarchical knowledge structure reduces the cost of expert labor and potentially makes language education more affordable and more engaging

Analisi della leggibilità dei consensi informati: un approccio linguistico-computazionale

In questa tesi è presentato un approccio linguistico-computazionale al problema della valutazione della leggibilità di consensi informati. È illustrata una metodologia di analisi della leggibilità di un corpus di consensi informati basata su strumenti di annotazione linguistica. È inoltre presentata una metodologia volta a porre le basi alla creazione di uno strumento per la valutazione della leggibilità valido per più lingue. Infine sono presentati i primi risultati di una metodologia di semplificazione di un consenso informato

Readability assessment and automatic text simplification, the analysis of basque complex structures

301 p.(eus); 217 (eng)Tesi-lan honetan, euskarazko testuen konplexutasuna eta sinplifikazioa automatikoki aztertzeko lehen urratsak egin ditugu. Testuen konplexutasuna aztertzeko, testuen sinplifikazio automatikoa helburu duten beste hizkuntzetako lanetan eta euskarazko corpusetan egindako azterketa linguistikoan oinarritu gara. Azterketa horietatik testuak automatikoki sinplifikatzeko oinarri linguistikoak ezarri ditugu. Konplexutasuna automatikoki analizatzeko, ezaugarri linguistikoetan eta ikasketa automatikoko tekniketan oinarrituta ErreXail sistema sortu eta inplementatu dugu.Horretaz gain, testuak automatikoki sinplifikatuko dituen Euskarazko Testuen Sinplifikatzailea (EuTS) sistemaren arkitektura diseinatu dugu, sistemaren modulu bakoitzean egingo diren eragiketak definituz eta, kasu-azterketa bezala,informazio biografikoa duten egitura parentetikoak sinplifikatuko dituen Biografix tresna eleaniztuna inplementatuz.Amaitzeko, Euskarazko Testu Sinplifikatuen Corpusa (ETSC) corpusa osatu dugu. Corpus hau baliatu dugu gure sinplifikaziorako azterketetatik ateratako hurbilpena beste batzuekin erkatzeko. Konparazio horiek egiteko, etiketatze-eskema bat ere definitu dugu

Analyzing Text Complexity and Text Simplification: Connecting Linguistics, Processing and Educational Applications

Reading plays an important role in the process of learning and knowledge acquisition for both children and adults. However, not all texts are accessible to every prospective reader. Reading difficulties can arise when there is a mismatch between a reader’s language proficiency and the linguistic complexity of the text they read. In such cases, simplifying the text in its linguistic form while retaining all the content could aid reader comprehension. In this thesis, we study text complexity and simplification from a computational linguistic perspective. We propose a new approach to automatically predict the text complexity using a wide range of word level and syntactic features of the text. We show that this approach results in accurate, generalizable models of text readability that work across multiple corpora, genres and reading scales. Moving from documents to sentences, We show that our text complexity features also accurately distinguish different versions of the same sentence in terms of the degree of simplification performed. This is useful in evaluating the quality of simplification performed by a human expert or a machine-generated output and for choosing targets to simplify in a difficult text. We also experimentally show the effect of text complexity on readers’ performance outcomes and cognitive processing through an eye-tracking experiment. Turning from analyzing text complexity and identifying sentential simplifications to generating simplified text, one can view automatic text simplification as a process of translation from English to simple English. In this thesis, we propose a statistical machine translation based approach for text simplification, exploring the role of focused training data and language models in the process. Exploring the linguistic complexity analysis further, we show that our text complexity features can be useful in assessing the language proficiency of English learners. Finally, we analyze German school textbooks in terms of their linguistic complexity, across various grade levels, school types and among different publishers by applying a pre-existing set of text complexity features developed for German

An Automatic Modern Standard Arabic Text Simplification System: A Corpus-Based Approach

This thesis brings together an overview of Text Readability (TR) about Text Simplification (TS) with an application of both to Modern Standard Arabic (MSA). It will present our findings on using automatic TR and TS tools to teach MSA, along with challenges, limitations, and recommendations about enhancing the TR and TS models. Reading is one of the most vital tasks that provide language input for communication and comprehension skills. It is proved that the use of long sentences, connected sentences, embedded phrases, passive voices, non- standard word orders, and infrequent words can increase the text difficulty for people with low literacy levels, as well as second language learners. The thesis compares the use of sentence embeddings of different types (fastText, mBERT, XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. The accuracy of the 3-way CEFR (The Common European Framework of Reference for Languages Proficiency Levels) classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification, respectively and 0.71 Spearman correlation for the regression task. At the same time, the binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for the sentence-pair semantic similarity classifier. TS is an NLP task aiming to reduce the linguistic complexity of the text while maintaining its meaning and original information (Siddharthan, 2002; Camacho Collados, 2013; Saggion, 2017). The simplification study experimented using two approaches: (i) a classification approach and (ii) a generative approach. It then evaluated the effectiveness of these methods using the BERTScore (Zhang et al., 2020) evaluation metric. The simple sentences produced by the mT5 model achieved P 0.72, R 0.68 and F-1 0.70 via BERTScore while combining Arabic- BERT and fastText achieved P 0.97, R 0.97 and F-1 0.97. To reiterate, this research demonstrated the effectiveness of the implementation of a corpus-based method combined with extracting extensive linguistic features via the latest NLP techniques. It provided insights which can be of use in various Arabic corpus studies and NLP tasks such as translation for educational purposes