Abstract
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, 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- Anthology ID:
- 2022.semeval-1.27
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 217–220
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.27
- DOI:
- 10.18653/v1/2022.semeval-1.27
- Cite (ACL):
- Lis Pereira and Ichiro Kobayashi. 2022. OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 217–220, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection (Pereira & Kobayashi, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.semeval-1.27.pdf