Papers by Javid Ebrahimi
HotFlip: White-Box Adversarial Examples for Text Classification (P18-2)
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| Challenge: | Existing methods to create adversarial examples without explicit knowledge of model parameters are not effective. |
| Approach: | They propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier by an atomic flip operation. |
| Outcome: | The proposed method can be adapted to attack a word-level classifier with a few constraints. |
MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)
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| Challenge: | Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance . |
| Approach: | They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages. |
| Outcome: | The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research . |
How Can Self-Attention Networks Recognize Dyck-n Languages? (2020.findings-emnlp)
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| Challenge: | Recent work has explored the generalized Dyck-n (Dn) languages . |
| Approach: | They compare the performance of two variants of self-attention networks for Dyck-n (Dn) languages with a starting symbol. |
| Outcome: | The proposed model can generalize to longer sequences and deeper dependencies. |
On Adversarial Examples for Character-Level Neural Machine Translation (C18-1)
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| Challenge: | Using adversarial examples to measure robustness of deep learning models has become a standard procedure due to the difficulty of creating white-box adversarials for discrete text input. |
| Approach: | They propose two novel attacks which aim to remove or change a word in a translation, rather than simply break the NMT. |
| Outcome: | The proposed attacks are significantly stronger than their black-box counterparts in different attack scenarios, showing more serious vulnerabilities than previously known. |