OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection
Lis Kanashiro Pereira, Ichiro Kobayashi · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
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 multilingual 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 6 th place in SubTask A (zeroshot) setting and 15 th place in SubTask A (oneshot) setting.