Papers with Text Complexity

10 papers
The iRead4Skills Intelligent Complexity Analyzer (2025.emnlp-demos)

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Challenge: 20% of EU adult population exhibits low-literacy and numeracy skills (EA, 2021).
Approach: iRead4Skills Intelligent Complexity Analyzer integrates a range of NLP components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French.
Outcome: The system assigns four tailored difficulty levels and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity.
Arabic Curriculum Analysis (2020.coling-demos)

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Challenge: Effective language curricula are critical to teaching communication skills . a platform that analyzes curricular content can help identify shortcomings .
Approach: They propose a platform that analyzes Arabic curricula and provides insights into their content.
Outcome: The proposed system analyzes Arabic curricula and provides insights into their content . it provides statistics about word usage and morphological forms in different grades .
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (2022.findings-naacl)

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Challenge: Complex word identification (CWI) aims to identify words in a text that are difficult for a reader to understand and therefore benefit from simplification.
Approach: They propose to use a novel active learning framework to tailor models to individual readers and release a dataset of complexity annotations and models as a benchmark for further research.
Outcome: The proposed model can be tailored to individual readers and released as a benchmark for future research.
Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays (2026.acl-demo)

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Challenge: Existing Arabic writing technologies primarily use a single quality score for essays, but there is limited support for Arabic AES.
Approach: They propose a Web-based platform that integrates Arabic AES workflows with a user-friendly interface.
Outcome: The proposed system integrates with existing Arabic scoring systems and provides a user-friendly interface.
Analysis of Language Change in Collaborative Instruction Following (2021.findings-emnlp)

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Challenge: Prior work has found that language complexity is reduced along multiple dimensions as conventions are formed.
Approach: They analyze language change over time in a collaborative task where utility-maximizing participants form conventions and increase their expertise.
Outcome: The study shows that instructors increase language complexity along dimensions to collaborate with skill followers.
ILDAE: Instance-Level Difficulty Analysis of Evaluation Data (2022.acl-long)

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Challenge: Instance-level difficulty analysis of evaluation data is a new field of research that focuses on leveraging instance difficulty in natural language processing.
Approach: They conduct Instance-Level Difficulty Analysis of Evaluation data in a large-scale setup of 23 datasets and demonstrate its five novel applications.
Outcome: The proposed model improves efficiency and accuracy, improves quality and improves Out-of-Domain performance.
BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages (2023.emnlp-main)

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Challenge: Current research on automatic readability assessment (ARA) has focused on improving the performance of models in high-resource languages such as English.
Approach: They propose a hierarchical cross-lingual modeling approach that takes advantage of a language’s placement in the family tree to increase the amount of available training data.
Outcome: The proposed model improves the performance of models in high-resource languages such as English and Hiligaynon, minasbate, Karay-a, and Rinconada.
CEFR-Based Sentence Difficulty Annotation and Assessment (2022.emnlp-main)

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Challenge: Controllable text simplification is a crucial assistive technique for language learning and teaching.
Approach: They propose a sentence-level assessment model to handle unbalanced level distribution . previous studies have suggested that controllable text simplification is difficult to apply .
Outcome: The proposed method outperforms baselines in readability assessment by scoring macro-F1 on the level assessment.
Estimating Lexical Complexity from Document-Level Distributions (2024.lrec-main)

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Challenge: Existing methods for complexity estimation are limited to entire documents . health assessment tools are too short for existing methods to apply .
Approach: They propose a two-step approach for estimating lexical complexity that does not rely on pre-annotated data.
Outcome: The proposed method is tested on the Norwegian language and compares with other assessment tools.
CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts (2020.lrec-1)

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Challenge: Traditional text complexity assessment only takes into account lexical and lexiconal complexity.
Approach: They propose a tool for automatic assessment of conceptual text complexity based on the current state-of-the-art unsupervised approach . they compare the current implementation with the state of the art and discuss the influence of the choice of entity linker on the performance of the tool.
Outcome: The proposed tool can be personalized and adapted to the needs of struggling readers.

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