Text Readability Assessment for Second Language Learners

This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and $\textit{PCC}$ of 0.938

Readability Analysis of Text in English Textbooks of Federal Board of Intermediate and Secondary Education for Higher Secondary Level

This study mainly focuses on the readability of the text of the HSSC-II textbook. Considering the importance of textbook evaluation, the study evaluated the English textbook of HSSC-II, which is taught at all the colleges affiliated with the Federal Board of Intermediate and Secondary Education (FBISE). Selected texts from the book have been analyzed so as to determine their readability. The texts were analyzed through an online text evaluation tool, the Text Readability Consensus Calculator (TRCC), which determines the grade level, text readability level, and age of the reader as well as the appropriation of the text. The readability scores of the text yielded by the TRCC were compared with the students\u27 results of the comprehension test. This comparison reveals that the original text is "fairly difficult to read" for 13–15-year-old native readers among 8th and 9th grade students, while the evaluator rates the simplified text as "fairly easy to read" for native readers among 12–14-year-old 7th and 8th grade students. While the results of the comprehension test for non-native readers show that the text is extremely difficult, non-native 12th graders with an average age of 17 could only get 39% in the original text and 47.6% in the simplified text. The research concludes with some suggestions for the text designer to consider various features for making text readability compatible with the grade level of the students

Investigating Indonesian Textbooks Readability Using Fry Graph Formula

Learning the Indonesian language cannot be separated from the use of textbooks. In using textbooks, teachers must consider several aspects, especially text readability. This study aims to measure text readability in Indonesian textbooks using the Fry Graph Formula and determine the suitability of the text used in the book with the student's school level. The present study applied the descriptive analysis and quantitative approach method to count and analyze texts in several Indonesian language textbooks readability using the Fry graph formula. Three Indonesian language textbooks with 45 measured text in 11th grade were analyzed. This study found that 25 texts showed the results "Not Appropriate", 16 texts showed the results "Appropriate", and four texts showed the results "Invalid". Based on the analysis, the number of readabilities of text in Indonesian language textbooks was easy for textbooks at 11th grade. The findings in this study can help the teacher adjust and choose the suitable teaching materials in Indonesian language textbook to improve teaching and learning quality. Before selecting a leading textbook as a learning source, we suggest teachers analyze text readability to improve learning efficiency and quality.

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

Analyzing the readability of articles has been an important sociolinguistic task. Addressing this task is necessary to the automatic recommendation of appropriate articles to readers with different comprehension abilities, and it further benefits education systems, web information systems, and digital libraries. Current methods for assessing readability employ empirical measures or statistical learning techniques that are limited by their ability to characterize complex patterns such as article structures and semantic meanings of sentences. In this paper, we propose a new and comprehensive framework which uses a hierarchical self-attention model to analyze document readability. In this model, measurements of sentence-level difficulty are captured along with the semantic meanings of each sentence. Additionally, the sentence-level features are incorporated to characterize the overall readability of an article with consideration of article structures. We evaluate our proposed approach on three widely-used benchmark datasets against several strong baseline approaches. Experimental results show that our proposed method achieves the state-of-the-art performance on estimating the readability for various web articles and literature.Comment: ECIR 202