Text Readability Assessment for Second Language Learners

Xia, Menglin; Kochmar, Ekaterina; Briscoe, Ted

doi:10.18653/v1/w16-0502

Cited by 98 publications

(131 citation statements)

References 23 publications

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“…For example, the Flesch-Kincaid index is a representative empirical measure defined as a linear combination of these factors [4]. Some later approaches mainly focus on proposing new features with the latest CohMetrix 3.0 [36] providing 108 features, and they combine and use the features using either linear functions or statistical models such as Support Vector Machines or multilayer perceptron [12,40,41,43,51]. While these approaches have shown some merits, they also lead to several drawbacks.…”

Section: A More Generic Description From the Simple English Wikipediamentioning

confidence: 99%

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

Meng

Chen

Mao

et al. 2020

Lecture Notes in Computer Science

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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.C. Meng-This work was done during the summer internships of CM and MC at Google, Mountain View. We thank the anonymous reviewers for their insightful comments.

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Section: A More Generic Description From the Simple English Wikipediamentioning

confidence: 99%

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

Meng

Chen

Mao

et al. 2020

Lecture Notes in Computer Science

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“…Feng et al (2010) and (Xia et al, 2016). LR models are linear models and can be thought of as singlelayer perceptrons with softmax or sigmoid activation functions.…”

Section: Methodsmentioning

confidence: 99%

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Tetreault

Burstein

Leacock³

et al. 2017

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Since the first workshop in 1997, BEA has become the leading venue for sharing and publishing innovative work that uses NLP to develop educational applications. The consistent interest and growth of the workshop has clear ties to challenges in education. The research presented at the workshop highlights advances in the technology and the maturity of the field of NLP in education. The capabilities serve as a response to educational challenges and are poised to support the needs of a variety of stakeholders, including educators, learners, parents, and administrators.NLP capabilities now support an array of learning domains, including writing, speaking, reading, and mathematics. In the writing and speech domains, automated writing evaluation (AWE) and speech assessment applications, respectively, are commercially deployed in high-stakes assessment and instructional settings, including Massive Open Online Courses (MOOCs). We also see widelyused commercial applications for plagiarism detection and peer review and explosive growth of mobile applications for game-based applications for instruction and assessment. The current educational and assessment landscape continues to foster a strong interest and high demand that pushes the state of the art in AWE capabilities to expand the analysis of written responses to writing genres other than those traditionally found in standardized assessments, especially writing tasks requiring use of sources and argumentative discourse.Steady growth in the development of NLP-based applications for education has prompted an increased number of workshops that typically focus on a single subfield. In BEA, we make an effort to have papers from many subfields, for example, tools for automated scoring, automated test-item generation, curriculum development, evaluation of text, dialogue, evaluation of genres beyond essays, feedback studies, and grammatical error correction.This year we received a record 62 submissions, and accepted 9 papers as oral presentations and 25 as poster presentation and/or demos, for an overall acceptance rate of 55 percent. Each paper was reviewed by three members of the Program Committee who were believed to be most appropriate for each paper. We continue to have a very strong policy to deal with conflicts of interest. First, we made a concerted effort to not assign papers to reviewers to evaluate if the paper had an author from their institution. Second, with respect to the organizing committee, authors of papers for which there was a conflict of interest recused themselves from the discussions.While the field is growing, we do recognize that there is a core group of institutions and researchers who work in this area. With a higher acceptance rate, we were able to include papers from a wider variety of topics and institutions. The papers accepted were selected on the basis of several factors, including the relevance to a core educational problem space, the novelty of the approach or domain, and the strength of the research. The accepted papers were highly diverse -...

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“…The readability prediction system for financial documents presented by Bonsall IV, Leone, Miller, and Rennekamp () is based on features such as the presence of active voice or number of hidden verbs. Changing the perspective, Vajjala and Meurers () studied readability assessment with second‐language acquisition in mind, a fact also considered by Xia, Kochmar, and Briscoe (). Both studies consider a wide variety of lexical and syntactic features and focus on English as their target language.…”

Section: Related Workmentioning

confidence: 99%

Is cross‐lingual readability assessment possible?

Azpiazu

Pera

2019

Asso for Info Science & Tech

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Most research efforts related to automatic readability assessment focus on the design of strategies that apply to a specific language. These state‐of‐the‐art strategies are highly dependent on linguistic features that best suit the language for which they were intended, constraining their adaptability and making it difficult to determine whether they would remain effective if they were applied to estimate the level of difficulty of texts in other languages. In this article, we present the results of a study designed to determine the feasibility of a cross‐lingual readability assessment strategy. For doing so, we first analyzed the most common features used for readability assessment and determined their influence on the readability prediction process of 6 different languages: English, Spanish, Basque, Italian, French, and Catalan. In addition, we developed a cross‐lingual readability assessment strategy that serves as a means to empirically explore the potential advantages of employing a single strategy (and set of features) for readability assessment in different languages, including interlanguage prediction agreement and prediction accuracy improvement for low‐resource languages.

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Text Readability Assessment for Second Language Learners

Cited by 98 publications

References 23 publications

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Is cross‐lingual readability assessment possible?

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