We present the MultiplEYE Text Corpus, a large-scale, document-level, multi-parallel resource designed to advance cross-linguistic research on reading and language processing. The corpus provides paragraph-level alignment for texts in 39 languages spanning seven language families and seven scripts. Unlike many existing multilingual corpora, a substantial number of documents were originally written in languages other than English, reducing English-centric bias and supporting more typologically diverse investigations. The texts are carefully selected to balance linguistic richness with experimental feasibility, particularly for eye-tracking-while-reading studies. Developed within a multi-lab initiative, the MultiplEYE Text Corpus follows unified translation, alignment, and experimental design guidelines to ensure cross-linguistic comparability. Its inclusion of texts varying in type and difficulty enables research on discourselevel processing, genre effects, and individual differences across a wide range of languages. The text corpus and accompanying metadata provide a robust foundation for multilingual psycholinguistic and computational modeling research. Data and materials are publicly available at https://doi.org/10.23668/psycharchives.21750.

This system description paper describes our participation in CMCL 2021 shared task on predicting human reading patterns. Our focus in this study is making use of well-known,traditional oculomotor control models and machine learning systems. We present experiments with a traditional oculomotor control model (the EZ Reader) and two machine learning models (a linear regression model and a re-current network model), as well as combining the two different models. In all experiments we test effects of features well-known in the literature for predicting reading patterns, such as frequency, word length and predictability. Our experiments support the earlier findings that such features are useful when combined. Furthermore, we show that although machine learning models perform better in comparison to traditional models, combination of both gives a consistent improvement for predicting multiple eye tracking variables during reading.