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Reading is a complex process which requires proper understanding of texts inorder to create coherent mental representations. However, comprehensionproblems may arise due to hard-to-understand sections, which can provetroublesome for readers, while accounting for their specific language skills.As such, steps towards simplifying these sections can be performed, byaccurately identifying and evaluating difficult structures. In this paper, wedescribe our approach for the SemEval-2021 Task 1: Lexical ComplexityPrediction competition that consists of a mixture of advanced NLP techniques,namely Transformer-based language models, pre-trained word embeddings, GraphConvolutional Networks, Capsule Networks, as well as a series of hand-craftedtextual complexity features. Our models are applicable on both subtasks andachieve good performance results, with a MAE below 0.07 and a Personcorrelation of .73 for single word identification, as well as a MAE below 0.08and a Person correlation of .79 for multiple word targets. Our results are just5.46% and 6.5% lower than the top scores obtained in the competition on thefirst and the second subtasks, respectively.

UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction