Idioms are expressions with non-literal and non-compositional meanings. For this reason, they pose a unique challenge for various NLP tasks including Machine Translation and Sentiment Analysis. In this paper, we propose an approach to clustering idioms in different languages by their sense. We leverage pre-trained cross-lingual transformer models and fine-tune them to produce cross-lingual vector representations of idioms according to their sense.