  Complex Word Identification (CWI) is a task centered on detecting
hard-to-understand words, or groups of words, in texts from different areas of
expertise. The purpose of CWI is to highlight problematic structures that
non-native speakers would usually find difficult to understand. Our approach
uses zero-shot, one-shot, and few-shot learning techniques, alongside
state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e.,
Transformers). Our aim is to provide evidence that the proposed models can
learn the characteristics of complex words in a multilingual environment by
relying on the CWI shared task 2018 dataset available for four different
languages (i.e., English, German, Spanish, and also French). Our approach
surpasses state-of-the-art cross-lingual results in terms of macro F1-score on
English (0.774), German (0.782), and Spanish (0.734) languages, for the
zero-shot learning scenario. At the same time, our model also outperforms the
state-of-the-art monolingual result for German (0.795 macro F1-score).
