OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection

AbstractWe 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, knowledgebase, or additional datasets other than the target datasets, our model achieved competitive results and ranked 6thplace in SubTask A (zero-shot) setting and 15thplace in SubTask A (one-shot) setting