mlsanityData to 7 Oct 2026

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

arXiv 2206.03025 ↗First posted 7 Jun 2022cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 17 references that are in the map. First posted on arXiv 7 Jun 2022.

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References · 15

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  11. 2019Robust Neural Machine Translation with Doubly Adversarial Inputscited by 105
  12. 2022SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embeddingcited by 36
3 more
  1. 2020The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understandingcited by 28
  2. 2021AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Modelscited by 27
  3. 2020Adversarial Training for Commonsense Inferencecited by 13

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