RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning
| Autor: | Asl, Javad Rafiei, Panzade, Prajwal, Blanco, Eduardo, Takabi, Daniel, Cai, Zhipeng |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, Our framework achieves a significant reduction in the success rate of various adversarial attacks, notably reducing the BERTAttack success rate by almost half (from 75.51\% to 38.81\%). The framework also yields improvements of 1.59\% and 0.23\% in semantic textual similarity tasks and various transfer tasks, respectively. Comment: Accepted at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL Findings) 2024. [https://openreview.net/forum?id=9dEAg4lJEA] |
| Databáze: | arXiv |
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