Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model

Evaluating the readability of a text can significantly facilitate the precise expression of information in a written form. The formulation of text readability assessment demands the identification of meaningful properties of the text and correct conversion of features to the right readability level. Sophisticated features and models are being used to evaluate the comprehensibility of texts accurately. Still, these models are challenging to implement, heavily language-dependent, and do not perform well on short texts. Deep reinforcement learning models are demonstrated to be helpful in further improvement of state-of-the-art text readability assessment models. The main contributions of the proposed approach are the automation of feature extraction, loosening the tight language dependency of text readability assessment task, and efficient use of text by finding the minimum portion of a text required to assess its readability. The experiments on Weebit, Cambridge Exams, and Persian readability datasets display the model's state-of-the-art precision, efficiency, and the capability to be applied to other languages.Comment: 8 pages, 2 figures, 6 equations, 7 table

A leitura, o texto e o Programa Nacional Biblioteca na Escola: intrincada rela??o para o processo de constru??o da compreens?o em leitura

Reading is a complex activity that requires from the reader great cognitive effort and for its realization the reader, the text and the context are necessary (LEFFA, 1996). Among these elements, the text is highlighted in this research because it is an instrument of teaching work. Researchers point out that linguistic and extralinguistic factors are important in determining the degree of textual complexity (McNamara and Graesser, Louresers, 2012) and influence the comprehension of the text through the context experienced by the reader (FUZER, CABRAL, 2014). Therefore, knowing the textual complexity of the texts used for reading in school is an important factor for the teacher to create work strategies. Our goal is to analyze the textual complexity of fourteen works that compose the literary collection of the National Library in the School Program (PNBE 2014), intended for early childhood education (category 2) and first years of basic education (category 3), seeking to understand the relations between two indices of the complexity of the text. To establish the textual complexity, we use the methodology proposed by Eggins (2004) that generates indicators of lexical density and syntactic complexity, adding to its analysis the textual modalities, the genre, and the textual record. The results suggest that the evaluated studies show a slight increase of lexical density when comparing the categories with each other. The texts intended for early childhood education (category 2) are more related to oral mode compared to the texts for the early years of elementary school (category 3). The texts for the initial years of elementary education (category 3) have more information in the same sentence, resulting in a greater cognitive effort to achieve comprehension The analysis of the related text and context reveals the dependence of the text in relation to the image in the evaluated works. These factors corroborate the great work involved in reading teaching-learning process and the need to think of tools to support the work of teachers. Knowing the textual complexity is essential for the teacher to make the choices objectively and safely, giving the texts appropriate to each of the stages of development of the student reader.RESUMO A Leitura ? uma atividade complexa que requer do leitor grande esfor?o cognitivo. Para sua realiza??o s?o elementos necess?rios: o leitor, o texto e o contexto (LEFFA, 1996). Entre esses, o texto recebe destaque nessa pesquisa, por ser instrumento do trabalho docente. Pesquisas apontam que fatores lingu?sticos e extralingu?sticos s?o importantes para determinar o grau de complexidade textual (McNAMARA; GRAESSER; LOUWERSE, 2012) al?m de influenciarem a compreens?o do texto por meio do contexto vivenciado pelo leitor (FUZER; CABRAL, 2014). Por isso, conhecer a complexidade textual dos textos utilizados para a leitura na escola ? fator importante para que o professor possa criar estrat?gias de trabalho. Nosso objetivo ? analisar a complexidade textual de quatorze obras que comp?em o acervo liter?rio do Programa Nacional Biblioteca na Escola (PNBE 2014), destinados ? educa??o infantil (categoria 2) e aos anos iniciais do ensino fundamental (categoria 3), buscando compreender as rela??es entre dois ?ndices de complexidade do texto. Para estabelecer a complexidade textual, utilizamos a metodologia proposta por Eggins (2004), que gera indicadores de densidade lexical e complexidade sint?tica, somando ? sua an?lise ?s modalidades textuais, o g?nero e o registro textual. Os resultados obtidos sugerem que as obras avaliadas apresentam um aumento t?mido de densidade lexical, quando comparadas ?s categorias entre si. Os textos destinados ? educa??o infantil (categoria 2) apresentam maior rela??o com a modalidade oral quando comparados aos textos destinados aos anos iniciais do ensino fundamental (categoria 3). Os textos destinados aos anos iniciais do ensino fundamental (categoria 3) apresentam maior n?mero de informa??es em uma mesma ora??o, resultando em maior esfor?o cognitivo para atingir a compreens?o. A an?lise da rela??o texto e contexto revela a depend?ncia do texto em rela??o ? imagem nas obras avaliadas. Esses fatores corroboram o grande trabalho envolvido no processo ensino-aprendizagem da leitura e a necessidade de se pensar em instrumentos que amparem o trabalho do professor. Conhecer a complexidade textual ? essencial para que o professor possa fazer as escolhas de forma objetiva e segura, destinando os textos apropriados a cada uma das etapas de desenvolvimento do aluno leitor

Revisiting the Readability Assessment of Texts in Portuguese

Abstract. The Web content accessibility guidelines (WCAG) 2.0 include in its principle of comprehensibility an accessibility requirement related to the level of writing. This requirement states that websites with texts demanding higher reading skills than individuals with lower secondary education possess (fifth to ninth grades in Brazil) should offer them an alternative version of the same content. Natural Language Processing technology and research in Psycholinguistics can help automate the task of classifying a text according to its reading difficulty. In this paper, we present experiments to build a readability checker to classify texts in Portuguese, considering different text genres, domains and reader ages, using naturally occurring texts. More precisely, we classify texts in simple (for 7 to 14-year-olds) and complex (for adults), and address three key research questions: (1) Which machine-learning algorithm produces the best results? (2) Which features are relevant? (3) Do different text genres have an impact on readability assessment