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Open AccessArticle
Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings
by
Vidhu Mathur
Vidhu Mathur 1
,
Tanvi Dadu
Tanvi Dadu 2 and
Swati Aggarwal
Swati Aggarwal 3,*
1
Department of Computer Science Engineering, Maharaja Surajmal Institute of Technology Affiliated to GGSIPU, New Delhi 110058, India
2
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
3
Faculty of Logistics, Molde University College, Britvegen 2, 6410 Molde, Norway
*
Author to whom correspondence should be addressed.
Submission received: 17 May 2024
/
Revised: 14 June 2024
/
Accepted: 17 June 2024
/
Published: 23 June 2024
Featured Application
Featured Application: The application of this research is to create better multilingual datasets by utilizing the insights gained from our investigation into mBART and XLM-Roberta. These improved datasets will support the creation of more robust and accurate AI NLP models that can effectively handle various languages, enhancing performance in tasks like machine translation, sentiment analysis, text categorization, and information retrieval. This research addresses biases and limitations in the current translation methods.
Abstract
Cross-lingual transfer learning using multilingual models has shown promise for improving performance on natural language processing tasks with limited training data. However, translation can introduce superficial patterns that negatively impact model generalization. This paper evaluates two state-of-the-art multilingual models, Cross-Lingual Model-Robustly Optimized BERT Pretraining Approach (XLM-Roberta) and Multilingual Bi-directional Auto-Regressive Transformer (mBART), on the cross-lingual natural language inference (XNLI) natural language inference task using both original and machine-translated evaluation sets. Our analysis demonstrates that translation can facilitate cross-lingual transfer learning, but maintaining linguistic patterns is critical. The results provide insights into the strengths and limitations of state-of-the-art multilingual natural language processing architectures for cross-lingual understanding.
Share and Cite
MDPI and ACS Style
Mathur, V.; Dadu, T.; Aggarwal, S.
Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings. Appl. Sci. 2024, 14, 5440.
https://0-doi-org.brum.beds.ac.uk/10.3390/app14135440
AMA Style
Mathur V, Dadu T, Aggarwal S.
Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings. Applied Sciences. 2024; 14(13):5440.
https://0-doi-org.brum.beds.ac.uk/10.3390/app14135440
Chicago/Turabian Style
Mathur, Vidhu, Tanvi Dadu, and Swati Aggarwal.
2024. "Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings" Applied Sciences 14, no. 13: 5440.
https://0-doi-org.brum.beds.ac.uk/10.3390/app14135440
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