Wafa Aissa, Raquel Amaro, David Antunes, Thibault Bañeras-Roux, Jorge Baptista, Alejandro Catala, Luís Correia, Thomas François, Marcos Garcia, Mario Izquierdo-Álvarez, Nuno Mamede, Vasco Martins, Miguel Neves, Eugénio Ribeiro, Sandra Rodriguez Rey, Elodie Vanzeveren
| 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. |
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| Challenge: | a cognitively motivated method for evaluating the inflectional complexity of a language is proposed . authors argue that some languages are inflectionally more complex than others . |
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When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features (2020.lrec-1)
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| Challenge: | Existing approaches to automatic assessment of text complexity focus on syntactic and lexical complexity. |
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| Challenge: | Existing cloze-style benchmarks for language models lack specific, granular areas of knowledge and often rely on templates that can bias models. |
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Comprehensive Multi-Dataset Evaluation of Reading Comprehension (D19-58)
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| Challenge: | Recent research aims to facilitate training and evaluation on several reading comprehension datasets at the same time. |
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| Challenge: | Existing open IE systems were based on handcrafted features or fine-grained rules. |
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FABRA: French Aggregator-Based Readability Assessment toolkit (2022.lrec-1)
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Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, Thomas François
| Challenge: | a large number of readability predictor variables are used to predict reading difficulty of texts . the most important predictors for native texts are lexical diversity, dependency counts and text coherence . |
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