  We propose a multilingual adversarial training model for determining whether
a sentence contains an idiomatic expression. Given that a key challenge with
this task is the limited size of annotated data, our model relies on
pre-trained contextual representations from different multi-lingual
state-of-the-art transformer-based language models (i.e., multilingual BERT and
XLM-RoBERTa), and on adversarial training, a training method for further
enhancing model generalization and robustness. Without relying on any
human-crafted features, knowledge bases, or additional datasets other than the
target datasets, our model achieved competitive results and ranked 6th place in
SubTask A (zero-shot) setting and 15th place in SubTask A (one-shot) setting.
