Simplified COVID-19 guidance for adults with intellectual and developmental disabilities.

BACKGROUND: During the COVID-19 pandemic, the United States' Centers for Disease Control and Prevention (CDC) created guidance documents that were too complex to be read and understood by the majority of adults with intellectual and developmental disabilities who often read at or below a third-grade reading level. This study explored the extent to which these adults could read and understand CDC documents simplified using Minimised Text Complexity Guidelines. METHOD: This study involved 20 participants, 18-48 years of age. Participants read texts and responded to multiple-choice items and open-ended questions to gather information about how they interacted with and understood the texts. RESULTS: The results provide initial evidence that the Minimised Text Complexity Guidelines resulted in texts that participants could read and understand. CONCLUSION: Implications for increasing the accessibility of public health information so that it can be read and understood by adults with extremely low literacy skills are discussed.

The Adjustment of Complexity on Sarcasm Processing in Chinese: Evidence from Reading Time Indicators.

It is controversial whether sarcasm processing should go through literal meaning processing. There is also a lack of eye movement evidence for Chinese sarcasm processing. In this study, we used eye movement experiments to explore the processing differences between sarcastic and literal meaning in Chinese text and whether this was regulated by sentence complexity. We manipulated the variables of complexity and literality. We recorded 33 participants' eye movements when they were reading Chinese text and the results were analyzed by a linear mixed model. We found that, in the early stage of processing, there was no difference between the processing time of the sarcastic meaning and the literal meaning of simple remarks, whereas for complex remarks, the time needed to process the sarcastic meaning was longer than that needed to process the literal meaning. In the later stage of processing, regardless of complexity, the processing time of the sarcastic meaning was longer than that of the literal meaning. These results suggest that sarcastic speech processing in Chinese is influenced by literal meaning, and the effect of literal meaning on sarcastic remarks is regulated by complexity. Sarcastic meaning was expressed differently in different stages of processing. These results support the hierarchical salience hypothesis of the serial modular model.

Bioinformatics tools for the sequence complexity estimates.

We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.

Sentence-level complexity in Russian: An evaluation of BERT and graph neural networks.

Introduction: Sentence-level complexity evaluation (SCE) can be formulated as assigning a given sentence a complexity score: either as a category, or a single value. SCE task can be treated as an intermediate step for text complexity prediction, text simplification, lexical complexity prediction, etc. What is more, robust prediction of a single sentence complexity needs much shorter text fragments than the ones typically required to robustly evaluate text complexity. Morphosyntactic and lexical features have proved their vital role as predictors in the state-of-the-art deep neural models for sentence categorization. However, a common issue is the interpretability of deep neural network results. Methods: This paper presents testing and comparing several approaches to predict both absolute and relative sentence complexity in Russian. The evaluation involves Russian BERT, Transformer, SVM with features from sentence embeddings, and a graph neural network. Such a comparison is done for the first time for the Russian language. Results and discussion: Pre-trained language models outperform graph neural networks, that incorporate the syntactical dependency tree of a sentence. The graph neural networks perform better than Transformer and SVM classifiers that employ sentence embeddings. Predictions of the proposed graph neural network architecture can be easily explained.

Clear, easy, plain, and simple as keywords for text simplification.

In this paper, we distinguish between four interconnected notions that recur in the literature on text simplification: clarity, easiness, plainness, and simplicity. While plain language and easy language have both been the subject of standardization efforts, there are few attempts to define text clarity and text simplicity. Indeed, in the definition of plain language, clarity has been favored at the expense of simplicity but is employed as a self-evident notion. Meanwhile, text simplicity suffers from a negative connotation and is more likely to be defined by its antonym, text complexity. In our analysis, we examine the current definitions of plain language and easy language and discuss common definitions of text clarity and text complexity. We propose a model of text simplification that can clarify the transition from specialized texts to plain language texts, and easy language texts. It is our contention that text simplification should be placed in a more general framework of discursive ergonomics.

Executive functions and components of oral reading fluency through the lens of text complexity.

As readers struggle to coordinate various reading- and language-related skills during oral reading fluency (ORF), miscues can emerge, especially when processing complex texts. Following a miscue, students often self-correct as a strategy to potentially restore ORF and online linguistic comprehension. Executive functions (EF) are hypothesized to play an interactive role during ORF. Yet, the role of EF in self-corrections while reading complex texts remains elusive. To this end, we evaluated the relation between students' probability of self-correcting miscues-or P(SC)-and their EF profile in a cohort of 143 participants (aged 9-15) who represented a diverse spectrum of reading abilities. Moreover, we used experimentally-manipulated passages (decoding, vocabulary, syntax, and cohesion) and employed a fully cross-classified mixed-effects multilevel regression strategy to evaluate the interplay between components of ORF, EF, and text complexity. Our results revealed that, after controlling for reading and language abilities, increased production of miscues across different passage conditions was explained by worse EF. We also found that students with better EF exhibited greater P(SC) when reading complex texts. While text complexity taxes students' EF and influences their production of miscues, findings suggest that EF may be interactively recruited to restore ORF via self-correcting oral reading errors. Overall, our results suggest that domain-general processes (e.g., EF) are associated with production of miscues and may underlie students' behavior of self-corrections, especially when reading complex texts. Further understanding of the relation between different components of ORF and cognitive processes may inform intervention strategies to improve reading proficiency and overall academic performance.

Exploring the use of network meta-analysis in education: examining the correlation between ORF and text complexity measures.

Calls for empirical investigations of the Common Core standards (CCSSs) for English Language Arts have been widespread, particularly in the area of text complexity in the primary grades (e.g., Hiebert & Mesmer Educational Research, 42(1), 44-51, 2013). The CCSSs mention that qualitative methods (such as Fountas and Pinnell) and quantitative methods (such as Lexiles) can be used to gauge text complexity (CCSS Initiative, 2010). However, researchers have questioned the validity of these tools for several decades (e.g., Hiebert & Pearson, 2010). In an effort to establish criterion validity of these tools, individual studies have compared how well they correlate with actual student reading performance measures, most commonly reading comprehension and/or oral-reading fluency (ORF). ORF is a key aspect of reading success and as such is often used for progress monitoring purposes. However, to date, studies have not been able to evaluate different text complexity tools and relation to reading outcomes across studies. This is challenging because the pair-wise meta-analytic model is not able to synthesize several independent variables that differ both within and across studies. Therefore, it is unable to answer pressing research questions in education, such as, which text complexity tool is most correlated with student ORF (and, thus, a good measure of text difficulty)? This question is timely given that the Common Core State Standards explicitly mention various text complexity tools; yet, the validity of such tools has been repeatedly questioned by researchers. This article provides preliminary evidence to answer that question using an approach borrowed from the field of medicine-Network Meta-Analysis (NMA; Lumley Statistics in Medicine, 21, 2313-2324, 2002). A systematic search yielded 5 studies using 19 different text complexity tools with ORF as the reading outcome measured. Both a frequentist and Bayesian NMA were conducted to pool the correlations of a given text complexity tool with students' ORF. While the results differed slightly across the two approaches, there is preliminary evidence in support of the hypothesis that text complexity tools which incorporate more fine-grained sub-lexical variables were more strongly correlated with student outcomes. While the results of this example cannot be generalized due to the low sample size, this article shows how NMA is a promising new analytic tool for synthesizing educational research.