  Reading is a complex process which requires proper understanding of texts in
order to create coherent mental representations. However, comprehension
problems may arise due to hard-to-understand sections, which can prove
troublesome for readers, while accounting for their specific language skills.
As such, steps towards simplifying these sections can be performed, by
accurately identifying and evaluating difficult structures. In this paper, we
describe our approach for the SemEval-2021 Task 1: Lexical Complexity
Prediction competition that consists of a mixture of advanced NLP techniques,
namely Transformer-based language models, pre-trained word embeddings, Graph
Convolutional Networks, Capsule Networks, as well as a series of hand-crafted
textual complexity features. Our models are applicable on both subtasks and
achieve good performance results, with a MAE below 0.07 and a Person
correlation of .73 for single word identification, as well as a MAE below 0.08
and a Person correlation of .79 for multiple word targets. Our results are just
5.46% and 6.5% lower than the top scores obtained in the competition on the
first and the second subtasks, respectively.
