Papers with natural language domain
Differentially Private Instance Encoding against Privacy Attacks (2022.naacl-srw)
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| Challenge: | TextHide is a proposed privacy-enhancing technology to protect the training data from privacy attacks. |
| Approach: | They propose to encode training data via instance encoding in natural language domain without theoretic privacy guarantee. |
| Outcome: | The proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off). |
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020.emnlp-main)
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| Challenge: | Data augmentation is a common method used to improve out-of-domain (OOD) generalization. |
| Approach: | They propose a data augmentation method that uses corruption and reconstruction functions to move randomly on a manifold to generate training examples. |
| Outcome: | The proposed method outperforms existing methods and baseline models on both in-domain and OOD data and achieves gains of 0.8% on OOD Amazon reviews, 1.8% accuracy on OOO MNLI, and 1.4 BLEU on in- domain IWSLT14 German-English. |
Neuro-Symbolic Approaches for Text-Based Policy Learning (2021.emnlp-main)
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| Challenge: | Text-based games are important testbeds for reinforcement learning in the natural language domain. |
| Approach: | They propose a method that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. |
| Outcome: | The proposed method outperforms existing methods in RL using 5-10x fewer training games. |
Generating Natural Language Adversarial Examples (D18-1)
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| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |
R-AT: Regularized Adversarial Training for Natural Language Understanding (2022.findings-emnlp)
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| Challenge: | Currently, adversarial training is a popular and powerful regularization method in the natural language domain. |
| Approach: | They propose to regularize adversarial training via dropout by perturbing word embeddings . they find that R-AT can improve many models by reducing adversariality . |
| Outcome: | The proposed method can reduce the inconsistency between training and testing of models with dropout. |