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2021 SemEval SemEval 2021

PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training

Abstract

AbstractWe experiment with XLM RoBERTa for Word in Context Disambiguation in the Multi Lingual and Cross Lingual setting so as to develop a single model having knowledge about both settings. We solve the problem as a binary classification problem and also experiment with data augmentation and adversarial training techniques. In addition, we also experiment with a 2-stage training technique. Our approaches prove to be beneficial for better performance and robustness.

Authors

Harsh Goyal, Aadarsh Singh, Priyanshu Kumar

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Adversarial Learning Machine Learning > Application Areas > Data Augmentation

Keywords

binary classification data augmentation cross-lingual transfer adversarial training multilingual model word-in-context disambiguation
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