Hervé Jégou


pdf bib
Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo | Alexandre Sablayrolles | Hervé Jégou | Douwe Kiela
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We propose the first general-purpose gradient-based adversarial attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks, outperforming prior work in terms of adversarial success rate with matching imperceptibility as per automated and human evaluation. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.


pdf bib
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Armand Joulin | Piotr Bojanowski | Tomas Mikolov | Hervé Jégou | Edouard Grave
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a quadratic problem to learn a orthogonal matrix aligning a bilingual lexicon, and use a retrieval criterion for inference. In this paper, we propose an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. Our experiments on standard benchmarks show that our approach outperforms the state of the art on word translation, with the biggest improvements observed for distant language pairs such as English-Chinese.