# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 3 Related Work
### Eye-Tracking in Text Readability Assessment  
This section sheds light on some of the last five years' work on integrating eye-tracking data in readability prediction models using machine learning methods, benefiting from the relationship between eye movements and human reading.  
A \"readability\" score indicates a document's recommended audience [16]. Using users' eye movements allows more personalization when predicting reading levels; consequently, appropriate texts are recommended to readers by considering different factors [8; 15]. A number of studies have targeted this problem, such as [PERSON] et al. [15], [PERSON] et al. [12], and [PERSON] et al. [8]. Because reader effort is a significant factor in reading and comprehension, [PERSON] and [PERSON] [17] developed an approach for quantifying reading effort called \"scanpath complexity\". [PERSON] et al.'s [16] work aimed to detect reader-specific difficult words in a document.  
However, although using eye-tracking measures have been proven effective in examining human-computer interactions [12], gathering enough real-time reading behavior data and building human-annotated training corpora remains time and labor intensive [8]. Consequently, whereas eye-tracking reading times could be used as indicators of increased linguistic complexity [11], researchers have begun to look toward predicting reading times through linguistic and psycholinguistic features instead of taking them from the eye data directly. The authors of [8] noted that \"models based on automatically predicted reading times present themselves as an attractive alternative to the current methods\" to meet NLPdemands for automatic complexity measures. Examples of studies that use this concept are [PERSON] et al. [11], [PERSON] & [PERSON] [20; 41], and [PERSON] et al. [19].  
The reading research literature notes that words that are \"predictable\" take less time to read and may even be skipped over [69]. The quantification of this word prediction is known as a surprisal measure. Given this benefit, the surprisal psycholinguistics feature has received special attention for modeling human sentence processing. Motivated by the excellent perplexity results accomplished by the use of NNs [70], [PERSON] and [PERSON] [71] investigated the degree to which surprisal estimated from computational language models (LMs) could be trusted and how LM quality and type variations affect surprisal's predictive power. Another study by [PERSON] and [PERSON] [72] compared the performance of variants of recurrent neural networks (RNNs) in estimating word surprisal values for words used in texts that were shown in self-paced reading, eye-tracking, and electroencephalography experiments. [PERSON] and [PERSON] [69] compared RNNs, particularly gated recurrent units (GRUs), as representative of RNNs, and transformer-based LMs in predicting human reading data following the approach of [72]. Complementing and enhancing the previous research [69; 71; 72], [PERSON] et al. [70] performed a much deeper investigation of the models' prediction behaviors by studying how their language models affect the estimated surprisal and subsequently the estimated human gaze using different datasets with different sizes.